Markus Krauss (1,2), Rolf Burghaus (3), Jörg Lippert (3), Mikko Niemi (4,5), Andreas Schuppert (1,2), Stefan Willmann (1), Lars Kuepfer (1), C. Diedrich (1), Linus Görlitz (1)
(1) Bayer Technology Services GmbH, Computational Systems Biology, 51368 Leverkusen, Germany; (2) Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Schinkelstr. 2, 52062 Aachen, Germany; (3) Bayer Pharma AG, Clinical Pharmacometrics, 42117 Wuppertal, Germany; (4) Department of Clinical Pharmacology, University of Helsinki, Helsinki, Finland; (5) HUSLAB, Helsinki University Central Hospital, Helsinki,
Objectives: To combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models in order to provide a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. And to support knowledge based extrapolation to other drugs or populations.
Methods: PBPK models are based on a large amount of prior physiological and anthropometric information which is integrated into the model structure. Because the large-scale PBPK models that we use here explicitly distinguish between compound and patient specific properties, they allow for separation of physiological and drug-induced effects as it is needed for knowledge based extrapolation. In order to introduce a rigorous treatment of parameter variability into the methodology a Bayesian-PBPK approach, based on a Markov Chain Monte Carlo (MCMC) algorithm is pursued. In this way the PBPK model parameters are sampled along a Markov chain, which has the posterior distribution as its stationary distribution from which parameter variability is extracted. Bayesian approaches have already been used before in conjunction with PBPK modeling, especially in toxicological questions [1], but also for population PK [2]. However, often the PBPK models used were comparatively small and contained lumped parameters carrying mixed information, in contrast to the large-scale models that we use here.
Results: Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients [3]. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions and this subgroup stratification can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.
Conclusions: The presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.
References:
[1] F. Y. Bois, F.Y., M. Jamei, and H.J. Clewell; PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals; Toxicology, 2010. 278(3): p. 256-267.
[2] Yang, Y., X. Xu, and P.G. Georgopoulos; A Bayesian population PBPK model for multiroute chloroform exposure. Journal of Exposure Science and Environmental Epidemiology, 2009. 20(4): p. 326-341.
[3] M. Krauss et al.; Using Bayesian-PBPK Modeling for Assessment of Inter-Individual Variability and Subgroup Stratification; Submitted to In Silico Pharmacology.
Reference: PAGE 22 () Abstr 2878 [www.page-meeting.org/?abstract=2878]
Poster: New Modelling Approaches