M. Krauß (1,2), L. Kuepfer (1), M. Meyer (1), M. Block (1), L. Goerlitz (1)
(1) Bayer Technology Services GmbH, Technology Development, Enabling Technologies, Computational Systems Biology, Leverkusen, Germany; (2) Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany
Objectives: The assessment of inter-individual variability is a key aspect in physiology-based pharmacokinetic (PBPK) modeling. Physiological differences like age or blood protein content have to be considered since these factors contribute to the pharmacokinetic (PK) variation. Only if the population wide variation of parameters is known in detail, reliable extrapolations to other populations can be performed. Markov-Chain-Monte-Carlo (MCMC) approaches provide a state of the art method to determine such variations for better predictions of individualized PK [1, 2]. We here present a combined approach of PBPK modeling and MCMC for the identification of the distribution of both individual as well as substance-specific parameters in a pravastatin data set [3]. Our aim is to determine the main sources of variability of PK and identify whether homogeneous subpopulations exist.
Methods: PBPK models enable a comprehensive simulation of drug PK at the whole-body scale based on drug distribution models and extensive collections of physiological parameters. By integration of specific experimental data, models are used to analyze and investigate the expected PK in groups of healthy volunteers or patients by processing population simulations. Identifying parameter distributions requires a Bayesian formulation of the population PBPK approach. In order to analyze these models it is necessary to sample from the so-called “posterior” parameter distribution, i.e. how likely is a parameter value given the information contained in the measured data. Since these distributions are high-dimensional, Markov-Chain-Monte-Carlo (MCMC) algorithms are used. This approach is applied to a pravastatin example [3]. The resulting marginal posterior distributions are analyzed for multi-modality which is then compared to clinical data to identify homogeneous subpopulations.
Results: Analysis of the marginal posterior distributions identified clearance and metabolization processes as main sources of variation. Moreover, homogeneous subpopulations could be identified from the results which can be assigned to a polymorphism in gene SLCO1B1 encoding the hepatic organ anion transporter OATP1B1 [3].
Conclusions: The presented approach of combined PBPK-MCMC is a systematic approach to characterize inter-individual variability of physiological parameters. It allows the identification of main sources of PK-variability and the identification of clinically relevant homogeneous subpopulations.
References:
[1] Block M, Görlitz L, Happ C, Burghaus R, Lippert J (2010) Separating individual physiological variability from drug related properties using PBPK Modeling with PK-Sim® and MoBi® – Theophylline. PAGE 2010
[2] Bois FY, Jamei M, Clewell HJ (2010) PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals. Toxicology 278: 256-267
[3] Niemi M, Pasanen MK, Neuvonen PJ (2006) SLCO1B1 polymorphism and sex affect the pharmacokinetics of pravastatin but not fluvastatin. Clin Pharmacol Ther 80: 356-366
Reference: PAGE 21 () Abstr 2558 [www.page-meeting.org/?abstract=2558]
Poster: New Modelling Approaches