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Hierarchical Mechanistic Modelling of Clinical Pharmacokinetic Data Hierarchical mechanistic modelling of clinical pharmacokinetic data A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Medical and Human Sciences 2015 Thierry Wendling Manchester Pharmacy School Contents List of Figures ........................................................................................................ 6 List of Tables .......................................................................................................... 9 List of abbreviations ............................................................................................ 10 Abstract ................................................................................................................ 13 Declaration ........................................................................................................... 14 Copyright ............................................................................................................. 14 Acknowledgements .............................................................................................. 15 Chapter 1: General introduction ...................................................................... 16 1.1. Modelling and simulation in drug development ........................................ 17 1.1.1. The drug development process .......................................................... 17 1.1.2. The role of pharmacokinetic and pharmacodynamic modelling in drug development ................................................................................................... 18 1.2. Mechanistic modelling of clinical pharmacokinetic data .......................... 23 1.2.1. Physiologically-based pharmacokinetic models ................................ 23 1.2.2. Oral absorption models .................................................................... 26 1.2.3. Hierarchical pharmacokinetic modelling .......................................... 29 1.3. Modelling survival data in cancer research ............................................... 32 1.3.1. Pharmacodynamics in oncology........................................................ 32 1.3.2. Survival analysis ............................................................................... 33 1.4. Project objectives ..................................................................................... 38 1.5. List of manuscripts and author contribution statement .............................. 39 1.6. References ............................................................................................... 42 Chapter 2: Model-based evaluation of the impact of formulation and food intake on the complex oral absorption of mavoglurant in healthy subjects ...... 50 Chapter 3: Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics ........................................................ 51 2 Chapter 4: Reduction of a whole-body physiologically-based pharmacokinetic model to stabilise the Bayesian analysis of clinical data ..................................... 52 Chapter 5: Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data ............................................... 53 5.1. Abstract ................................................................................................... 54 5.1.1. Background ...................................................................................... 54 5.1.2. Methods ............................................................................................ 54 5.1.3. Results .............................................................................................. 54 5.1.4. Conclusions ...................................................................................... 54 5.2. Introduction ............................................................................................. 55 5.3. Materials and methods ............................................................................. 56 5.3.1. Data ................................................................................................. 56 5.3.2. Statistical analysis ............................................................................ 57 5.3.2.1. Screening risk factors ..................................................................... 57 5.3.2.2. Modelling tumour size time-series ................................................. 58 5.3.2.3. Survival analysis ............................................................................ 59 5.3.2.4. Validation ...................................................................................... 61 5.4. Results ..................................................................................................... 62 5.4.1. Baseline risk factors ......................................................................... 62 5.4.2. Tumour size time-series model .......................................................... 62 5.4.3. Survival analysis ............................................................................... 64 5.4.3.1. The PAR approach ......................................................................... 65 5.4.3.2. The COX approach ........................................................................ 66 5.4.4. Predictive performance ..................................................................... 67 5.5. Discussion ............................................................................................... 69 5.6. Acknowledgements .................................................................................. 72 5.7. Conflict of interest ................................................................................... 72 5.8. References ............................................................................................... 73 3 Chapter 6: General discussion .......................................................................... 76 Appendix A1: Supplementary material for Chapter 2 ....................................... 80 A1.1. Supplementary methods ............................................................................ 81 A1.1.1. Implementation of dose superimposition in NONMEM using a sum of inverse Gaussian functions as an input rate function ...................................... 81 A1.1.1.1. Comparison of the FORTRAN subroutine approach with the reference method, using simulations ........................................................... 81 A1.1.1.2. Verification of the partial derivatives defined in the FORTRAN subroutine .................................................................................................. 82 A1.2. Supplementary results ............................................................................... 83 A1.2.1. Individual goodness-of-fit plots for each formulation-food conditions 83 A1.2.2. Implementation of dose superimposition in NONMEM using a sum of inverse Gaussian functions as an input rate function ...................................... 85 A1.2.2.1. Comparison of the FORTRAN subroutine approach with a reference method using simulations ............................................................ 85 A1.2.2.2. Verification of the partial derivatives .......................................... 86 A1.3. User-defined FORTRAN functions FUNCA, FUNCB and FUNCC (subroutine referred as sumdoset_3IG.f90 in Appendix A1.4) ............................ 87 A1.4. Dose superposition in NONMEM using the user-supplied FORTRAN subroutine (example of the MR-fasted model using a sum of three IG functions as input rate function) ............................................................................................. 90 A1.5. Dose superposition performed solely in a NM-TRAN abbreviated code for mavoglurant MR-fasted model (reference method) ............................................. 92 A1.6. References ................................................................................................ 95 Appendix A2: Supplementary material for Chapter 3 ....................................... 96 A2.1. Prediction of mavoglurant hepatic intrinsic clearance from recombinant human cytochrome P450 enzyme kinetic data .................................................... 97 A2.2. Estimation of the formulation-specific dissolution constant z from in vitro dissolution profiles ............................................................................................. 99 4 A2.3. Prediction of mavoglurant effective permeability in human jejunum ....... 100 A2.4. Calculation of systemic bioavailability ................................................... 101 A2.5. Drug-drug interaction between mavoglurant and ketoconazole in adult subjects ............................................................................................................ 102 A2.6. Results of the Bayesian analysis of Study 1 data when estimating all drug- specific parameters ........................................................................................... 104 A2.7. References .............................................................................................. 104 Appendix A3: Supplementary material for Chapter 4 ..................................... 106 A3.1. Supplementary tables .............................................................................. 107 A3.2. Supplementary figures ............................................................................ 109 Appendix A4: Supplementary material for Chapter 5 ..................................... 112
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