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

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

10.1186/2193-9616-1-6

Projects: G: Clinical translation

In Silico Pharmacol
In Silico Pharmacol 1(1) : 6
2013

Markus Krauss, Rolf Burghaus, Jörg Lippert, Mikko Niemi, Pertti Neuvonen, Andreas Schuppert, Stefan Willmann, Lars Kuepfer, Linus Görlitz

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[Markus Krauß] [Rolf Burghaus] [Jörg Lippert] [Lars Küpfer]

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Liver and Organism
Views: 1698
  • Created: 16th Sep 2013 at 13:50
  • Last updated: 24th Oct 2013 at 16:15

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