Open Access Original research

Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification

Markus Krauss12, Rolf Burghaus3, Jörg Lippert3, Mikko Niemi45, Pertti Neuvonen5, Andreas Schuppert12, Stefan Willmann1, Lars Kuepfer1 and Linus Görlitz1*

Author Affiliations

1 Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368, Germany

2 Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Schinkelstr, Aachen, 2, 52062, Germany

3 Bayer Pharma AG, Clinical Pharmacometrics, Wuppertal, 42117, Germany

4 Department of Clinical Pharmacology, University of Helsinki, Helsinki, Finland

5 HUSLAB, Helsinki University Central Hospital, Helsinki, Finland

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In Silico Pharmacology 2013, 1:6  doi:10.1186/2193-9616-1-6

Published: 11 April 2013

Abstract

Purpose

Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.

Methods

PBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.

Results

Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which 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.

Keywords:
Physiologically-based pharmacokinetic modeling; Bayesian approaches; Markov chain Monte Carlo; Inter-individual variability; OATP1B1; Drug development; Pravastatin