title: Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification.
abstract: OBJECTIVE
  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.
authors.affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany ; RWTH Aachen, Schinkelstr, Aachen Institute for Advanced Study in Computational Engineering Sciences, Aachen, 2, 52062 Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Krauss
authors.affiliation: Clinical Pharmacometrics, Bayer Pharma AG, Wuppertal, 42117 Germany.
authors.forename: Rolf
authors.initials: R
authors.lastname: Burghaus
authors.affiliation: Clinical Pharmacometrics, Bayer Pharma AG, Wuppertal, 42117 Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Lippert
authors.affiliation: Department of Clinical Pharmacology, University of Helsinki, Helsinki, Finland ; HUSLAB, Helsinki University Central Hospital, Helsinki, Finland.
authors.forename: Mikko
authors.initials: M
authors.lastname: Niemi
authors.affiliation: HUSLAB, Helsinki University Central Hospital, Helsinki, Finland.
authors.forename: Pertti
authors.initials: P
authors.lastname: Neuvonen
authors.affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany ; RWTH Aachen, Schinkelstr, Aachen Institute for Advanced Study in Computational Engineering Sciences, Aachen, 2, 52062 Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schuppert
authors.affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Willmann
authors.affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany.
authors.forename: Lars
authors.initials: L
authors.lastname: Kuepfer
authors.affiliation: Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany.
authors.forename: Linus
authors.initials: L
authors.lastname: Görlitz
keywords: Bayesian approaches; Drug development; Inter-individual variability; Markov chain Monte Carlo; OATP1B1; Physiologically-based pharmacokinetic modeling; Pravastatin