Towards population physiology based pharmacokinetics modelling (popPBPK) in an industrial environment
C. Diedrich(1), R. Burghaus(1), J. Lippert(1)
(1) Bayer AG, Pharmaceuticals
Objectives: Establish Bayesian Population Physiology based Pharmacokinetics (popPBPK) workflow
Methods: Over the last decade whole body physiology based pharmacokinetics (PBPK) has become an established tool for rationalising drug absorption distribution metabolism and excretion (ADME) at a mechanistic level (see e.g. [1]). In order for these approaches to be applied in a statistically sound population approach we have established a Bayesian hierarchical modelling framework [2]. In this way, the vast available prior knowledge on physiological parameters can be incorporated into statistical inference of population PBPK (popPBPK) models. Sampling of the high dimensional (several hundred parameters) posterior distribution of the hierarchical model parameters is done using a tailored Markov Chain Monte Carlo algorithm.
Results: A workflow for setting up and performing popPBPK calculations based on physiological prior knowledge from the PK-Sim [3] data base as well as clinical PK data has been established and will be presented. The workflow will be applied to a typical use case. We will demonstrate that PBPK models can be qualified in a statistically rigorous fashion given (clinical) data, a model structure and prior knowledge using established population PK techniques. Additionally methods of information theory can be used in order to quantify differences between prior and posterior distribution. Using this approach full control of the information flow from clinical data into the popPBPK model is achieved. The popPBPK model is then used to break PK population variability as well as uncertainty down to physiological parameters.
Conclusions: A popPBPK workflow for deriving Bayesian hierarchical population models given prior knowledge, PBPK model structure and clinical data using MCMC sampling has been established. We thereby provide a statistically sound methodological framework for qualifying PBPK models against clinical data sets and measuring the flow of information from data into population models. The population models assessed in this way are then used for simulating untested scenarios with controlled Bayesian credible levels.
References:
[1] Kuepfer et al.; Pharmacometrics & Systems Pharmacology, (2016), 5, 516-531
[2] Krauss et al.; PLOS one, (2015), 10, e0139423 [3] Open Systems Pharmacology, www.open-systems-pharmacology.org