Biosimulation in drug development:
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WILEY-VCH
2008
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| Online Access: | http://deposit.dnb.de/cgi-bin/dokserv?id=2930683&prov=M&dok_var=1&dok_ext=htm Inhaltsverzeichnis |
| Physical Description: | XXVIII, 512 S. Ill., graph. Darst. 240 mm x 170 mm |
| ISBN: | 9783527316991 352731699X |
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| 650 | 4 | |a Computer Simulation | |
| 650 | 4 | |a Drug Design | |
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I V
Contents
Preface XVII
List of Contributors XXIII
Part I Introduction
1 Simulation in Clinical Drug Development 3
J. J. Perez Ruixo, F. De Ridder, H. Kimko, M. Samtani, E. Cox,
S. Mohanty and A. Vermeulen
1.1 Introduction 3
1.2 Models for Simulations 7
1.3 Simulations in Clinical Drug Development: Practical Examples 8
1.3.1 Predicting the Outcome of Phase I Studies of Erythropoietin
Receptor Agonists 8
1.3.2 Simulations for Antimicrobial Dose Selection 10
1.3.3 Optimizing the Design of Phase II Dose Finding Studies 14
1.3.4 Predicting the Outcome of Phase III Trials Using Phase II Data 19
1.4 Conclusions 23
2 Modeling of Complex Biomedical Systems 27
E. Mosekilde, C. Knudsen and]. L. Laugesen
2.1 Introduction 27
2.2 Pulsatile Secretion of Insulin 31
2.3 Subcutaneous Absorption of Insulin 38
2.4 Bursting Pancreatic /Ö Cells 43
2.5 Conclusions 52
3 Biosimulation of Drug Metabolism 59
M. Bertau, L. Brusch and U. Kummer
3.1 Introduction 59
3.2 Experimental Approaches 61
Biosimulation in Drug Development. Edited by Martin Bertau, Erik Mosekilde, and Hans V. Westerhoff
Copyright © 2008 WILEY VCH Verlag GmbH Co. KGaA, Weinheim
ISBN: 978 3 527 31699 1
VI I Contents
3.2.1 Animal Test Models 61
3.2.2 Microbial Models 61
3.3 The Biosimulation Approach 63
3.4 Ethical Issues 63
3.5 PharmBiosim a Computer Model of Drug Metabolism in Yeast 64
3.5.1 General Concept 64
3.5.1.1 Chemical Abstraction 64
3.5.1.2 Biological Abstraction 66
3.5.2 Initial Steps Experimental Results 67
3.5.2.1 Dehalogenation (Pathways II and III) 70
3.5.2.2 Retro Claisen Condensation (Pathway IV) 70
3.5.2.3 Ester Hydrolysis (Pathway VI) 70
3.5.2.4 Competing Pathways and Stereoselectivity 72
3.6 Computational Modeling 72
3.6.1 Selection of the Modeling Software 72
3.6.2 SBML compatible Software 73
3.6.2.1 Cellware 73
3.6.2.2 Copasi 73
3.6.2.3 Ecell 73
3.6.2.4 JigCell 73
3.6.2.5 JSim 73
3.6.2.6 Systems Biology Workbench 74
3.6.2.7 Virtual Cell 74
3.6.2.8 XPPAUT 74
3.6.3 CellML compatible Software 74
3.6.4 Kinetic Model 75
3.6.4.1 Methods 75
3.6.4.2 Model Derivation 76
3.6.4.3 Results 77
3.6.5 Stoichiometric Model 78
3.6.5.1 Methods 78
3.6.5.2 Model Derivation 78
3.6.5.3 Results 79
3.7 Application of the Model to Predict Drug Metabolism 79
3.8 Conclusions 80
Part II Simulating Cells and Tissues
4 Correlation Between In Vitro, In Situ, and In Vivo Models 89
I. Gonzälez Älvarez, V. Casabo, V. Merino and M. Bermejo
4.1 Introduction 89
4.2 Biophysical Models of Intestinal Absorption 91
4.2.1 Colon 92
Contents I VII
4.2.2 Small Intestine 92
4.2.3 Stomach 92
4.3 Influence of Surfactants on Intestinal Permeability 93
4.3.1 Absorption Experiments in Presence of Surfactants 94
4.3.1.1 Colon 94
4.3.1.2 Intestine 96
4.3.1.3 Stomach 96
4.4 Modeling and Predicting Fraction Absorbed from Permeability
Values 99
4.4.1 Mass Balance, Time independent Models 99
4.4.2 Prediction of the Fraction of Dose Absorbed from In Vitro and
In Situ Data 101
4.4.3 Prediction from In Situ Absorption Rate Constant Determined with
Closed Loop Techniques 101
4.4.4 Prediction from Permeabilities Through Caco 2 Cell Lines 102
4.4.5 Prediction from the PAMPA In Vitro System 104
4.5 Characterization of Active Transport Parameters 107
4.5.1 In Situ Parameter Estimation 107
4.5.2 In Vitro In Situ Correlation 109
5 Core Box Modeling in the Biosimulation of Drug Action 115
G. Cedersund, P. Strälfors and M. Jirstrand
5.1 Introduction 116
5.2 Core Box Modeling 117
5.2.1 Shortcomings ofGray Box and Minimal Modeling 117
5.2.1.1 Full Scale Mechanistic Gray Box Modeling 117
5.2.1.2 Minimal Modeling Using Hypothesis Testing 119
5.2.2 Outline of the Framework 121
5.2.3 Model Reduction to an Identifiable Core Model 121
5.2.3.1 Identifiability Analysis 123
5.2.3.2 Model Reduction 124
5.2.4 System Identification of the Core Model 126
5.2.4.1 Parameter Estimation 126
5.2.4.2 Model Quality Analysis 128
5.2.5 Back Translation to a Core Box Model 129
5.3 A Core Box Model for Insulin Receptor Phosphorylation and
Internalization in Adipocytes 132
5.4 Discussion 135
5.5 Summary 137
VIII I Contents
6 The Clucose Insulin Control System 141
C. E. Hallgreen, T. V. Korsgaard, R. N. Hansen and
M. Colding Jergensen
6.1 Introduction 142
6.1.1 Glucose and Insulin 142
6.1.2 Diabetes Mellitus 143
6.1.3 Biosimulation and Drug Development 144
6.1.4 The Glucose Insulin Control System 145
6.2 Biological Control Systems 146
6.2.1 Features of Biological Control 146
6.2.2 The Control System 147
6.2.3 Simple Control Types 148
6.2.3.1 Proportional Control 148
6.2.3.2 Integral Control 149
6.2.3.3 Differential Control 149
6.2.3.4 PID Control 150
6.2.3.5 Predictive Control 150
6.3 Glucose Sensing 151
6.3.1 Glucokinase 151
6.3.1.1 Glucose Phosphorylation 151
6.3.1.2 Translocation of Glucokinase 152
6.3.1.3 PI Control 154
6.3.2 The Beta Cell 155
6.3.2.1 First Phase of Insulin Secretion 156
6.3.3 The Liver Cell 158
6.3.4 The Hepato portal Sensor 159
6.3.5 Thelntestine 160
6.3.6 TheCNS 160
6.3.7 Conclusion 161
6.4 Glucose Handling 161
6.4.1 Glucose Intake 161
6.4.1.1 Plasma Glucose 162
6.4.2 Glucose Uptake 162
6.4.3 Brain 163
6.4.4 Liver 164
6.4.4.1 Gluconeogenesis 164
6.4.4.2 Hepatic Glucose Output and Gluconeogenesis 165
6.4.5 Muscle 169
6.4.5.1 Glucose Transport 169
6.4.5.2 Glucose Phosphorylation 170
6.4.5.3 The Fate of Glucose 6 phosphate 173
6.4.6 Adipocytes 177
6.4.6.1 Triglyceride and Free Fatty Acid 177
6.4.6.2 De Novo lipogenesis 179
6.4.7 Conclusion 181
Contents I IX
6.5 The Control System at Large 182
6.5.1 The Fasting State 182
6.5.1.1 Futile Cycles 183
6.5.2 Normal Meals 185
6.5.3 Glucose Tolerance Tests 185
6.5.3.1 Intravenous Glucose Tolerance Test 185
6.5.3.2 OGTTandMTT 187
6.5.4 The Glucose Clamp 187
6.5.4.1 Glucose Infusion Rate 187
6.5.4.2 Nutrition During Clamp 188
6.5.4.3 Clamp Level 188
6.6 Conclusions 190
6.6.1 Biosimulation 190
6.6.2 The Control System 191
6.6.3 Diabetes 191
6.6.4 Models and Medicines 192
7 Biological Rhythms in Mental Disorders 197
H. A. Braun, S. Postnova, B. Wollweber, H. Schneider, M. Belke,
K. Voigt, H. Murck, U. Hemmeter and M. T. Huber
7.1 Introduction: Mental Disorders as Multi scale and Multiple system
Diseases 197
7.2 The Time Course of Recurrent Mood Disorders: Periodic, Noisy
and Chaotic Disease Patterns 200
7.2.1 Transition Between Different Episode Patterns: The Conceptual
Approach 202
7.2.2 A Computer Model of Disease Patterns in Affective Disorders 203
7.2.3 Computer Simulations of Episode Sensitization with Autonomous
Disease Progression 205
7.3 Mood Related Disturbances of Circadian Rhythms:
Sleep Wake Cycles and HPA Axis 207
7.3.1 The HPA Axis and its Disturbances 207
7.3.2 Sleep EEG in Depression 209
7.3.3 Neurotransmitters and Hormones Controlling Sleep Pattern and
Mood 209
7.3.4 A Nonlinear Feedback Model of the HPA Axis with Circadian
Cortisol Peaks 210
7.4 Neuronal Rhythms: Oscillations and Synchronization 214
7.4.1 The Model Neuron: Structure and Equations 215
7.4.2 Single Neuron Impulse Patterns and Tonic to Bursting
Transitions 217
7A3 Network Synchronization in Tonic, Chaotic and Bursting
Regimes 219
X Contents
7.4.4 Synchronization Between Neurons at Different Dynamic States 220
7.5 Summary and Conclusions: The Fractal Dimensions of
Function 222
8 Energy Metabolism in Conformational Diseases 233
J. Ovädi and F. Orosz
8.1 Whatisthe Major Energy Sourceofthe Brain? 233
8.2 Unfolded/Misfolded Proteins Impair Energy Metabolism 238
8.3 Interactions of Glycolytic Enzymes with "Neurodegenerative
Proteins" 239
8.4 Post translational Modifications of Glycolytic Enzymes 242
8.5 Triosephosphate Isomerase Deficiency, a Unique Glycolytic
Enzymopathy 244
8.6 Microcompartmentation in Energy Metabolism 247
8.7 Concluding Remarks 250
9 Heart Simulation, Arrhythmia, and theActions ofDrugs 259
D. Noble
9.1 The Problem 259
9.2 Origin of the Problem 261
9.3 Avoiding the Problem 264
9.4 Multiple Cellular Mechanisms of Arrhythmia 265
9.5 Linking Levels: Building the Virtual Heart 268
Part III Technologies for Simulating Drug Action and Effect
10 Optimizing Temporal Patterns of Anticancer Drug Delivery by
Simulations of a Cell Cycle Automaton 275
A. Altinok, F. Levi and A. Goldbeter
10.1 Introduction 275
10.2 An Automaton Model for the Cell Cycle 277
10.2.1 Rules of the Cell Cycle Automaton 277
10.2.2 Distribution of Cell Cycle Phases 279
10.2.3 Coupling the Cell Cycle Automaton to the Circadian Clock 281
10.2.4 The Cell Cycle Automaton Model: Relation with Other Types of
Cellular Automata 282
10.3 Assessing the Efficacy of Circadian Delivery of the Anticancer Drug
5 FU 283
10.3.1 Mode of Action of 5 FU 283
10.3.2 Circadian Versus Continuous Administration of 5 FU 283
10.3.3 Circadian 5 FU Administration: Effect of Time of Peak Drug
Delivery 284
Contents I XI
10.3.4 EffectofVariabilityofCellCycle Phase Durations 288
10.4 Discussion 289
11 Probability of Exocytosis in Pancreatic ß Cells: Dependence on Ca2+
Sensing Latency Times, Ca2+ Channel Kinetic Parameters, and
Channel Clustering 299
J. Galvanovskis, P. Rorsman and B. Söderberg
11.1 Introduction 299
11.2 Theory 301
11.3 Mathematical Model 301
11.4 Dwell Time Distributions 302
11.5 Waiting Time Distribution 304
11.6 Average Waiting Time 305
11.7 Casesyv = 1,2, and 3 306
11.8 Numerical Simulations 307
11.9 Discussion 309
11.10 Conclusions 311
12 Modeling Kidney Pressure and Flow Regulation 313
O. V. Sosnovtseva, E. Mosekilde and N. H. Holstein Rathlou
12.1 Introduction 313
12.2 Experimental Background 317
12.3 Single nephron Model 320
12.4 Simulation Results 326
12.5 Intra nephron Synchronization Phenomena 333
12.6 Modeling of Coupled Nephrons 336
12.7 Experimental Evidence for Synchronization 341
12.8 Conclusion and Perspectives 343
13 Toward a Computational Model of Deep Brain Stimulation in
Parkinson's Disease 349
A. Beuter and]. Modolo
13.1 Introduction 349
13.2 Background 352
13.2.1 DBS Numbers, Stimulation Parameters and Effects 352
13.2.2 DBS and Basal Ganglia Circuitry 354
13.2.3 DBS: The Preferred Target Today 355
13.2.4 DBS: Paradox and Mechanisms 356
13.3 Population Density Based Model 357
13.3.1 Modeling Approach: Multiscaling 357
13.3.2 Model Equations 359
13.3.3 Synapses and the Population Density Approach 362
XII I Contents
13.3.4 Solving the Conservation Equation 364
13.3.5 Results and Simulations 364
13.4 Perspectives 367
13.5 Conclusion 368
14 Constructing a Virtual Proteasome 373
A. Zaikin, F. Luciani and]. Kurths
14.1 Experiment and Modeling 374
14.2 Finding the Cleavage Pattern 376
14.3 Possible Translocation Mechanism 377
14.4 Transport Model and Influence of Transport Rates on the Protein
Degradation 381
14.4.1 The Transport Model 381
14.4.2 Analytics Distribution of Peptide Lengths 382
14.4.2.1 One Cleavage Center 382
14.4.2.2 Two Cleavage Centers 383
14.4.2.3 Maximum in Peptide Length Distribution 384
14.5 Comparison with Numerical Results 385
14.5.1 Monotonously Decreasing Transport Rates 386
14.5.2 Nonmonotonous Transport Rates 387
14.6 Kinetic Model of the Proteasome 389
14.6.1 Themodel 389
14.6.2 Kinetics 392
14.6.3 Length Distribution of the Fragments 394
14.7 Discussion 395
14.7.1 Development of Modeling 395
14.7.2 Kinetics Models and Neurodegenerative Associated Proteasome
Degradation 397
Part IV Applications of Biosimulation
15 Silicon Cell Models: Construction, Analysis, and Reduction 403
F. J. Bruggeman, H. M. Härdin, J. H. van Schuppen and
H. V. Westerhoff
15.1 Introduction 403
15.2 Kinetic Models in Cell Biology: Purpose and Practice 406
15.3 Silicon Cell Models 408
15.4 Model Reduction by Balanced Truncation 409
15.5 Balanced Truncation in Practice 411
15.6 Balanced Truncation in Action: Reduction of a Silicon Cell Model
of Glycolysis in Yeast 414
15.7 Conclusions 418
Contents I XIII
16 Building Virtual Human Populations: Assessing the Propagation of
Cenetic Variability in Drug Metabolism to Pharmacokinetics and
Pharmacodynamics 425
G. L. Dickinson and A. Rostami Hodjegan
16.1 Introduction 425
16.2 ADME and Pharmacokinetics in Drug Development 426
16.2.1 Absorption 427
16.2.2 Distribution 427
16.2.3 Drug Metabolism 428
16.2.4 Excretion 428
16.3 Sources of Interindividual Variability in ADME 428
16.3.1 Pharmacokinetic Variability 430
16.3.1.1 Variability in Absorption 430
16.3.1.2 Variability in Distribution 431
16.3.1.3 Variability in Metabolism 431
16.3.1.4 Variability in Excretion 432
16.3.2 Pharmacodynamic Variability 432
16.3.3 Other Sources of Variability in Drug Response 433
16.4 Modeling and Simulation of ADME in Virtual Human
Population 433
16.4.1 The Need for More Efficient Clinical Trials 435
16.4.2 Current Clinical Trial Simulation in Drug Development 435
16.4.3 Incorporationof to Vitro Preclinical Data Into CTS 436
16.4.3.1 Prediction of Absorption 436
16.4.3.2 Prediction of Metabolism 436
16.4.3.3 Prediction of Efficacy/Toxicity 438
16.5 The Use of Virtual Human Populations for Simulating ADME 438
16.5.1 Assessing the Interindividual Variability of In Vivo Drug Clearance
from In Vitro Data 438
16.5.2 Prediction of Clearance and its Variability in Neonates, Infants, and
Children 439
16.5.2.1 Incorporating Information on Population Variability into
Mechanistic DM PK PD Modeling to Assess the Power of
Pharmacogenetic Studies 442
16.6 Conclusions 443
17 Biosimulation in Clinical Drug Development 447
T. Lehr, A. Staab and H. G. Schäfer
17.1 Introduction 447
17.2 Models in Clinical Development 448
17.2.1 Model Types 449
17.2.1.1 Complexityof Models 450
17.3 Clinical Drug Development 452
17.3.1 Phase I 452
XIV I Contents
17.3.2 Phase II 453
17.3.3 Phase III 454
17.3.4 Phase IV 454
17.4 Modeling Technique: Population Approach 455
17.4.1 Model Structure 456
17.4.1.1 Structural Model 456
17.4.1.2 Statistical Model 456
17.4.1.3 Covariate Model 458
17.4.1.4 Population Model 459
17.4.2 Parameter Estimation 459
17.4.3 Building Population Models 460
17.5 Pharmacokinetic Models 461
17.5.1 Empirical Pharmacokinetic Models 462
17.5.1.1 Example: NS2330 (Tesofensine) 462
17.5.1.2 Results 463
17.5.2 Mechanism based Pharmacokinetic Models 465
17.5.2.1 System Parameters 467
17.5.2.2 Drug dependent Parameters 467
17.5.2.3 Examples 468
17.6 Pharmacodynamic Models 468
17.6.1 Empirical Pharmacodynamic Models 468
17.6.1.1 Linking Pharmacokinetics and Pharmacodynamics 469
17.6.2 Mechanism based Pharmacodynamic Models 470
17.6.2.1 Examples 472
17.6.3 Semi mechanistic Models 473
17.6.3.1 Example: BIBN4096 473
17.6.3.2 Results 474
17.7 Disease Progression Models 475
17.8 Patient Models 476
17.8.1 Covariate Distribution Model 477
17.8.2 Compliance Model 477
17.8.3 Drop out Model 478
17.9 Outlook/Future Trends 478
17.10 Software 479
17.10.1 General Simulation Packages 479
17.10.2 PBPK Software 480
17.10.3 Population Approach Software 480
17.10.4 Clinical Trial Simulators 481
18 Biosimulation and Its Contribution to the Three Rs 485
H. Gürtler
18.1 Ethical Considerations in Drug Development 485
18.2 The Three Rs An Ethical Approach to Animal Experimentation 486
18.3 The Three Rs Alternatives 487
Contents I XV
18.3.1 Replacement Alternatives 487
18.3.2 Reduction Alternatives 487
18.3.3 Refinement Alternatives 488
18.4 The EU and the Three Rs 488
18.4.1 European Partnership 488
18.4.2 European Centre for the Validation of Alternatives 489
18.4.3 European Consensus Platform for Alternatives 490
18.5 Applying the Three Rs to Human Experimentation 490
18.6 Biosimulation and its Contribution to the Three Rs 491
18.6.1 Biosimulation A New Tool in Drug Development 491
18.6.2 The Challenges in Drug Development 491
18.6.3 Biosimulation's Contribution to Drug Development 493
18.6.4 Biosimulation's Contribution to the Three Rs 494
Index 497 |
| adam_txt |
I V
Contents
Preface XVII
List of Contributors XXIII
Part I Introduction
1 Simulation in Clinical Drug Development 3
J. J. Perez Ruixo, F. De Ridder, H. Kimko, M. Samtani, E. Cox,
S. Mohanty and A. Vermeulen
1.1 Introduction 3
1.2 Models for Simulations 7
1.3 Simulations in Clinical Drug Development: Practical Examples 8
1.3.1 Predicting the Outcome of Phase I Studies of Erythropoietin
Receptor Agonists 8
1.3.2 Simulations for Antimicrobial Dose Selection 10
1.3.3 Optimizing the Design of Phase II Dose Finding Studies 14
1.3.4 Predicting the Outcome of Phase III Trials Using Phase II Data 19
1.4 Conclusions 23
2 Modeling of Complex Biomedical Systems 27
E. Mosekilde, C. Knudsen and]. L. Laugesen
2.1 Introduction 27
2.2 Pulsatile Secretion of Insulin 31
2.3 Subcutaneous Absorption of Insulin 38
2.4 Bursting Pancreatic /Ö Cells 43
2.5 Conclusions 52
3 Biosimulation of Drug Metabolism 59
M. Bertau, L. Brusch and U. Kummer
3.1 Introduction 59
3.2 Experimental Approaches 61
Biosimulation in Drug Development. Edited by Martin Bertau, Erik Mosekilde, and Hans V. Westerhoff
Copyright © 2008 WILEY VCH Verlag GmbH Co. KGaA, Weinheim
ISBN: 978 3 527 31699 1
VI I Contents
3.2.1 Animal Test Models 61
3.2.2 Microbial Models 61
3.3 The Biosimulation Approach 63
3.4 Ethical Issues 63
3.5 PharmBiosim a Computer Model of Drug Metabolism in Yeast 64
3.5.1 General Concept 64
3.5.1.1 Chemical Abstraction 64
3.5.1.2 Biological Abstraction 66
3.5.2 Initial Steps Experimental Results 67
3.5.2.1 Dehalogenation (Pathways II and III) 70
3.5.2.2 Retro Claisen Condensation (Pathway IV) 70
3.5.2.3 Ester Hydrolysis (Pathway VI) 70
3.5.2.4 Competing Pathways and Stereoselectivity 72
3.6 Computational Modeling 72
3.6.1 Selection of the Modeling Software 72
3.6.2 SBML compatible Software 73
3.6.2.1 Cellware 73
3.6.2.2 Copasi 73
3.6.2.3 Ecell 73
3.6.2.4 JigCell 73
3.6.2.5 JSim 73
3.6.2.6 Systems Biology Workbench 74
3.6.2.7 Virtual Cell 74
3.6.2.8 XPPAUT 74
3.6.3 CellML compatible Software 74
3.6.4 Kinetic Model 75
3.6.4.1 Methods 75
3.6.4.2 Model Derivation 76
3.6.4.3 Results 77
3.6.5 Stoichiometric Model 78
3.6.5.1 Methods 78
3.6.5.2 Model Derivation 78
3.6.5.3 Results 79
3.7 Application of the Model to Predict Drug Metabolism 79
3.8 Conclusions 80
Part II Simulating Cells and Tissues
4 Correlation Between In Vitro, In Situ, and In Vivo Models 89
I. Gonzälez Älvarez, V. Casabo, V. Merino and M. Bermejo
4.1 Introduction 89
4.2 Biophysical Models of Intestinal Absorption 91
4.2.1 Colon 92
Contents I VII
4.2.2 Small Intestine 92
4.2.3 Stomach 92
4.3 Influence of Surfactants on Intestinal Permeability 93
4.3.1 Absorption Experiments in Presence of Surfactants 94
4.3.1.1 Colon 94
4.3.1.2 Intestine 96
4.3.1.3 Stomach 96
4.4 Modeling and Predicting Fraction Absorbed from Permeability
Values 99
4.4.1 Mass Balance, Time independent Models 99
4.4.2 Prediction of the Fraction of Dose Absorbed from In Vitro and
In Situ Data 101
4.4.3 Prediction from In Situ Absorption Rate Constant Determined with
Closed Loop Techniques 101
4.4.4 Prediction from Permeabilities Through Caco 2 Cell Lines 102
4.4.5 Prediction from the PAMPA In Vitro System 104
4.5 Characterization of Active Transport Parameters 107
4.5.1 In Situ Parameter Estimation 107
4.5.2 In Vitro In Situ Correlation 109
5 Core Box Modeling in the Biosimulation of Drug Action 115
G. Cedersund, P. Strälfors and M. Jirstrand
5.1 Introduction 116
5.2 Core Box Modeling 117
5.2.1 Shortcomings ofGray Box and Minimal Modeling 117
5.2.1.1 Full Scale Mechanistic Gray Box Modeling 117
5.2.1.2 Minimal Modeling Using Hypothesis Testing 119
5.2.2 Outline of the Framework 121
5.2.3 Model Reduction to an Identifiable Core Model 121
5.2.3.1 Identifiability Analysis 123
5.2.3.2 Model Reduction 124
5.2.4 System Identification of the Core Model 126
5.2.4.1 Parameter Estimation 126
5.2.4.2 Model Quality Analysis 128
5.2.5 Back Translation to a Core Box Model 129
5.3 A Core Box Model for Insulin Receptor Phosphorylation and
Internalization in Adipocytes 132
5.4 Discussion 135
5.5 Summary 137
VIII I Contents
6 The Clucose Insulin Control System 141
C. E. Hallgreen, T. V. Korsgaard, R. N. Hansen and
M. Colding Jergensen
6.1 Introduction 142
6.1.1 Glucose and Insulin 142
6.1.2 Diabetes Mellitus 143
6.1.3 Biosimulation and Drug Development 144
6.1.4 The Glucose Insulin Control System 145
6.2 Biological Control Systems 146
6.2.1 Features of Biological Control 146
6.2.2 The Control System 147
6.2.3 Simple Control Types 148
6.2.3.1 Proportional Control 148
6.2.3.2 Integral Control 149
6.2.3.3 Differential Control 149
6.2.3.4 PID Control 150
6.2.3.5 Predictive Control 150
6.3 Glucose Sensing 151
6.3.1 Glucokinase 151
6.3.1.1 Glucose Phosphorylation 151
6.3.1.2 Translocation of Glucokinase 152
6.3.1.3 PI Control 154
6.3.2 The Beta Cell 155
6.3.2.1 First Phase of Insulin Secretion 156
6.3.3 The Liver Cell 158
6.3.4 The Hepato portal Sensor 159
6.3.5 Thelntestine 160
6.3.6 TheCNS 160
6.3.7 Conclusion 161
6.4 Glucose Handling 161
6.4.1 Glucose Intake 161
6.4.1.1 Plasma Glucose 162
6.4.2 Glucose Uptake 162
6.4.3 Brain 163
6.4.4 Liver 164
6.4.4.1 Gluconeogenesis 164
6.4.4.2 Hepatic Glucose Output and Gluconeogenesis 165
6.4.5 Muscle 169
6.4.5.1 Glucose Transport 169
6.4.5.2 Glucose Phosphorylation 170
6.4.5.3 The Fate of Glucose 6 phosphate 173
6.4.6 Adipocytes 177
6.4.6.1 Triglyceride and Free Fatty Acid 177
6.4.6.2 De Novo lipogenesis 179
6.4.7 Conclusion 181
Contents I IX
6.5 The Control System at Large 182
6.5.1 The Fasting State 182
6.5.1.1 Futile Cycles 183
6.5.2 Normal Meals 185
6.5.3 Glucose Tolerance Tests 185
6.5.3.1 Intravenous Glucose Tolerance Test 185
6.5.3.2 OGTTandMTT 187
6.5.4 The Glucose Clamp 187
6.5.4.1 Glucose Infusion Rate 187
6.5.4.2 Nutrition During Clamp 188
6.5.4.3 Clamp Level 188
6.6 Conclusions 190
6.6.1 Biosimulation 190
6.6.2 The Control System 191
6.6.3 Diabetes 191
6.6.4 Models and Medicines 192
7 Biological Rhythms in Mental Disorders 197
H. A. Braun, S. Postnova, B. Wollweber, H. Schneider, M. Belke,
K. Voigt, H. Murck, U. Hemmeter and M. T. Huber
7.1 Introduction: Mental Disorders as Multi scale and Multiple system
Diseases 197
7.2 The Time Course of Recurrent Mood Disorders: Periodic, Noisy
and Chaotic Disease Patterns 200
7.2.1 Transition Between Different Episode Patterns: The Conceptual
Approach 202
7.2.2 A Computer Model of Disease Patterns in Affective Disorders 203
7.2.3 Computer Simulations of Episode Sensitization with Autonomous
Disease Progression 205
7.3 Mood Related Disturbances of Circadian Rhythms:
Sleep Wake Cycles and HPA Axis 207
7.3.1 The HPA Axis and its Disturbances 207
7.3.2 Sleep EEG in Depression 209
7.3.3 Neurotransmitters and Hormones Controlling Sleep Pattern and
Mood 209
7.3.4 A Nonlinear Feedback Model of the HPA Axis with Circadian
Cortisol Peaks 210
7.4 Neuronal Rhythms: Oscillations and Synchronization 214
7.4.1 The Model Neuron: Structure and Equations 215
7.4.2 Single Neuron Impulse Patterns and Tonic to Bursting
Transitions 217
7A3 Network Synchronization in Tonic, Chaotic and Bursting
Regimes 219
X Contents
7.4.4 Synchronization Between Neurons at Different Dynamic States 220
7.5 Summary and Conclusions: The Fractal Dimensions of
Function 222
8 Energy Metabolism in Conformational Diseases 233
J. Ovädi and F. Orosz
8.1 Whatisthe Major Energy Sourceofthe Brain? 233
8.2 Unfolded/Misfolded Proteins Impair Energy Metabolism 238
8.3 Interactions of Glycolytic Enzymes with "Neurodegenerative
Proteins" 239
8.4 Post translational Modifications of Glycolytic Enzymes 242
8.5 Triosephosphate Isomerase Deficiency, a Unique Glycolytic
Enzymopathy 244
8.6 Microcompartmentation in Energy Metabolism 247
8.7 Concluding Remarks 250
9 Heart Simulation, Arrhythmia, and theActions ofDrugs 259
D. Noble
9.1 The Problem 259
9.2 Origin of the Problem 261
9.3 Avoiding the Problem 264
9.4 Multiple Cellular Mechanisms of Arrhythmia 265
9.5 Linking Levels: Building the Virtual Heart 268
Part III Technologies for Simulating Drug Action and Effect
10 Optimizing Temporal Patterns of Anticancer Drug Delivery by
Simulations of a Cell Cycle Automaton 275
A. Altinok, F. Levi and A. Goldbeter
10.1 Introduction 275
10.2 An Automaton Model for the Cell Cycle 277
10.2.1 Rules of the Cell Cycle Automaton 277
10.2.2 Distribution of Cell Cycle Phases 279
10.2.3 Coupling the Cell Cycle Automaton to the Circadian Clock 281
10.2.4 The Cell Cycle Automaton Model: Relation with Other Types of
Cellular Automata 282
10.3 Assessing the Efficacy of Circadian Delivery of the Anticancer Drug
5 FU 283
10.3.1 Mode of Action of 5 FU 283
10.3.2 Circadian Versus Continuous Administration of 5 FU 283
10.3.3 Circadian 5 FU Administration: Effect of Time of Peak Drug
Delivery 284
Contents I XI
10.3.4 EffectofVariabilityofCellCycle Phase Durations 288
10.4 Discussion 289
11 Probability of Exocytosis in Pancreatic ß Cells: Dependence on Ca2+
Sensing Latency Times, Ca2+ Channel Kinetic Parameters, and
Channel Clustering 299
J. Galvanovskis, P. Rorsman and B. Söderberg
11.1 Introduction 299
11.2 Theory 301
11.3 Mathematical Model 301
11.4 Dwell Time Distributions 302
11.5 Waiting Time Distribution 304
11.6 Average Waiting Time 305
11.7 Casesyv = 1,2, and 3 306
11.8 Numerical Simulations 307
11.9 Discussion 309
11.10 Conclusions 311
12 Modeling Kidney Pressure and Flow Regulation 313
O. V. Sosnovtseva, E. Mosekilde and N. H. Holstein Rathlou
12.1 Introduction 313
12.2 Experimental Background 317
12.3 Single nephron Model 320
12.4 Simulation Results 326
12.5 Intra nephron Synchronization Phenomena 333
12.6 Modeling of Coupled Nephrons 336
12.7 Experimental Evidence for Synchronization 341
12.8 Conclusion and Perspectives 343
13 Toward a Computational Model of Deep Brain Stimulation in
Parkinson's Disease 349
A. Beuter and]. Modolo
13.1 Introduction 349
13.2 Background 352
13.2.1 DBS Numbers, Stimulation Parameters and Effects 352
13.2.2 DBS and Basal Ganglia Circuitry 354
13.2.3 DBS: The Preferred Target Today 355
13.2.4 DBS: Paradox and Mechanisms 356
13.3 Population Density Based Model 357
13.3.1 Modeling Approach: Multiscaling 357
13.3.2 Model Equations 359
13.3.3 Synapses and the Population Density Approach 362
XII I Contents
13.3.4 Solving the Conservation Equation 364
13.3.5 Results and Simulations 364
13.4 Perspectives 367
13.5 Conclusion 368
14 Constructing a Virtual Proteasome 373
A. Zaikin, F. Luciani and]. Kurths
14.1 Experiment and Modeling 374
14.2 Finding the Cleavage Pattern 376
14.3 Possible Translocation Mechanism 377
14.4 Transport Model and Influence of Transport Rates on the Protein
Degradation 381
14.4.1 The Transport Model 381
14.4.2 Analytics Distribution of Peptide Lengths 382
14.4.2.1 One Cleavage Center 382
14.4.2.2 Two Cleavage Centers 383
14.4.2.3 Maximum in Peptide Length Distribution 384
14.5 Comparison with Numerical Results 385
14.5.1 Monotonously Decreasing Transport Rates 386
14.5.2 Nonmonotonous Transport Rates 387
14.6 Kinetic Model of the Proteasome 389
14.6.1 Themodel 389
14.6.2 Kinetics 392
14.6.3 Length Distribution of the Fragments 394
14.7 Discussion 395
14.7.1 Development of Modeling 395
14.7.2 Kinetics Models and Neurodegenerative Associated Proteasome
Degradation 397
Part IV Applications of Biosimulation
15 Silicon Cell Models: Construction, Analysis, and Reduction 403
F. J. Bruggeman, H. M. Härdin, J. H. van Schuppen and
H. V. Westerhoff
15.1 Introduction 403
15.2 Kinetic Models in Cell Biology: Purpose and Practice 406
15.3 Silicon Cell Models 408
15.4 Model Reduction by Balanced Truncation 409
15.5 Balanced Truncation in Practice 411
15.6 Balanced Truncation in Action: Reduction of a Silicon Cell Model
of Glycolysis in Yeast 414
15.7 Conclusions 418
Contents I XIII
16 Building Virtual Human Populations: Assessing the Propagation of
Cenetic Variability in Drug Metabolism to Pharmacokinetics and
Pharmacodynamics 425
G. L. Dickinson and A. Rostami Hodjegan
16.1 Introduction 425
16.2 ADME and Pharmacokinetics in Drug Development 426
16.2.1 Absorption 427
16.2.2 Distribution 427
16.2.3 Drug Metabolism 428
16.2.4 Excretion 428
16.3 Sources of Interindividual Variability in ADME 428
16.3.1 Pharmacokinetic Variability 430
16.3.1.1 Variability in Absorption 430
16.3.1.2 Variability in Distribution 431
16.3.1.3 Variability in Metabolism 431
16.3.1.4 Variability in Excretion 432
16.3.2 Pharmacodynamic Variability 432
16.3.3 Other Sources of Variability in Drug Response 433
16.4 Modeling and Simulation of ADME in Virtual Human
Population 433
16.4.1 The Need for More Efficient Clinical Trials 435
16.4.2 Current Clinical Trial Simulation in Drug Development 435
16.4.3 Incorporationof to Vitro Preclinical Data Into CTS 436
16.4.3.1 Prediction of Absorption 436
16.4.3.2 Prediction of Metabolism 436
16.4.3.3 Prediction of Efficacy/Toxicity 438
16.5 The Use of Virtual Human Populations for Simulating ADME 438
16.5.1 Assessing the Interindividual Variability of In Vivo Drug Clearance
from In Vitro Data 438
16.5.2 Prediction of Clearance and its Variability in Neonates, Infants, and
Children 439
16.5.2.1 Incorporating Information on Population Variability into
Mechanistic DM PK PD Modeling to Assess the Power of
Pharmacogenetic Studies 442
16.6 Conclusions 443
17 Biosimulation in Clinical Drug Development 447
T. Lehr, A. Staab and H. G. Schäfer
17.1 Introduction 447
17.2 Models in Clinical Development 448
17.2.1 Model Types 449
17.2.1.1 Complexityof Models 450
17.3 Clinical Drug Development 452
17.3.1 Phase I 452
XIV I Contents
17.3.2 Phase II 453
17.3.3 Phase III 454
17.3.4 Phase IV 454
17.4 Modeling Technique: Population Approach 455
17.4.1 Model Structure 456
17.4.1.1 Structural Model 456
17.4.1.2 Statistical Model 456
17.4.1.3 Covariate Model 458
17.4.1.4 Population Model 459
17.4.2 Parameter Estimation 459
17.4.3 Building Population Models 460
17.5 Pharmacokinetic Models 461
17.5.1 Empirical Pharmacokinetic Models 462
17.5.1.1 Example: NS2330 (Tesofensine) 462
17.5.1.2 Results 463
17.5.2 Mechanism based Pharmacokinetic Models 465
17.5.2.1 System Parameters 467
17.5.2.2 Drug dependent Parameters 467
17.5.2.3 Examples 468
17.6 Pharmacodynamic Models 468
17.6.1 Empirical Pharmacodynamic Models 468
17.6.1.1 Linking Pharmacokinetics and Pharmacodynamics 469
17.6.2 Mechanism based Pharmacodynamic Models 470
17.6.2.1 Examples 472
17.6.3 Semi mechanistic Models 473
17.6.3.1 Example: BIBN4096 473
17.6.3.2 Results 474
17.7 Disease Progression Models 475
17.8 Patient Models 476
17.8.1 Covariate Distribution Model 477
17.8.2 Compliance Model 477
17.8.3 Drop out Model 478
17.9 Outlook/Future Trends 478
17.10 Software 479
17.10.1 General Simulation Packages 479
17.10.2 PBPK Software 480
17.10.3 Population Approach Software 480
17.10.4 Clinical Trial Simulators 481
18 Biosimulation and Its Contribution to the Three Rs 485
H. Gürtler
18.1 Ethical Considerations in Drug Development 485
18.2 The Three Rs An Ethical Approach to Animal Experimentation 486
18.3 The Three Rs Alternatives 487
Contents I XV
18.3.1 Replacement Alternatives 487
18.3.2 Reduction Alternatives 487
18.3.3 Refinement Alternatives 488
18.4 The EU and the Three Rs 488
18.4.1 European Partnership 488
18.4.2 European Centre for the Validation of Alternatives 489
18.4.3 European Consensus Platform for Alternatives 490
18.5 Applying the Three Rs to Human Experimentation 490
18.6 Biosimulation and its Contribution to the Three Rs 491
18.6.1 Biosimulation A New Tool in Drug Development 491
18.6.2 The Challenges in Drug Development 491
18.6.3 Biosimulation's Contribution to Drug Development 493
18.6.4 Biosimulation's Contribution to the Three Rs 494
Index 497 |
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| index_date | 2024-07-02T19:13:41Z |
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| spelling | Biosimulation in drug development ed. by Martin Bertau ... Weinheim WILEY-VCH 2008 XXVIII, 512 S. Ill., graph. Darst. 240 mm x 170 mm txt rdacontent n rdamedia nc rdacarrier Computer Simulation Drug Design Drug development Simulation methods Models, Theoretical Biomathematik (DE-588)4139408-2 gnd rswk-swf Arzneimittelentwicklung (DE-588)4143176-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content (DE-588)4173536-5 Patentschrift gnd-content Arzneimittelentwicklung (DE-588)4143176-5 s Biomathematik (DE-588)4139408-2 s DE-604 Bertau, Martin 1968- Sonstige (DE-588)118007483 oth http://deposit.dnb.de/cgi-bin/dokserv?id=2930683&prov=M&dok_var=1&dok_ext=htm HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016226560&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Biosimulation in drug development Computer Simulation Drug Design Drug development Simulation methods Models, Theoretical Biomathematik (DE-588)4139408-2 gnd Arzneimittelentwicklung (DE-588)4143176-5 gnd |
| subject_GND | (DE-588)4139408-2 (DE-588)4143176-5 (DE-588)4143413-4 (DE-588)4173536-5 |
| title | Biosimulation in drug development |
| title_auth | Biosimulation in drug development |
| title_exact_search | Biosimulation in drug development |
| title_exact_search_txtP | Biosimulation in drug development |
| title_full | Biosimulation in drug development ed. by Martin Bertau ... |
| title_fullStr | Biosimulation in drug development ed. by Martin Bertau ... |
| title_full_unstemmed | Biosimulation in drug development ed. by Martin Bertau ... |
| title_short | Biosimulation in drug development |
| title_sort | biosimulation in drug development |
| topic | Computer Simulation Drug Design Drug development Simulation methods Models, Theoretical Biomathematik (DE-588)4139408-2 gnd Arzneimittelentwicklung (DE-588)4143176-5 gnd |
| topic_facet | Computer Simulation Drug Design Drug development Simulation methods Models, Theoretical Biomathematik Arzneimittelentwicklung Aufsatzsammlung Patentschrift |
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