Bayesian Whole Body Population Physiologically Based Pharmacokinetic Approach for Characterization of Interindividual Variability of Diazepam
Tsiros Periklis (1), Tsiliki Georgia (1), Dokoumetzidis Aristeidis (2), Sarimveis Haralambos(1)
(1) School of Chemical Engineering, National Technical University of Athens, Greece, (2) School of Pharmacy, University of Athens, Greece
Objectives: To develop a Bayesian population approach for an existing whole-body physiologically based pharmacokinetic (WBPBPK) model of Diazepam in order to identify the main sources of variability, through integration of prior knowledge of selected physiological parameters.
Methods: Plasma concentrations-time data from 12 healthy male and female volunteers in [1] were analysed using a well-structured, fourteen-compartment WBPBPK model [2]. A Bayesian hierarchical model, implemented in Stan V.2.14.0 [3], is employed considering the data at an individual level and identifying the variability in physiological parameters at a population level. The parameters of the model were of primarily physiological nature, namely blood flows and tissue volumes. Parameters were selected by combining two sensitivity analysis methodologies, i.e. pertubation analysis of the compartmental matrix and Sobol’s sensitivity indices. The physiological parameters that were found to be insignificant, as well as the drug-specific parameters, were given fixed values. The values of the latter ones were retrieved from the posterior analysis of a previous study [2]. Additionally, population covariates were considered as part of the 3-stage hierarchical model developed.
Results: We have found that the presented model adequately describes the experimental plasma concentration-time profiles. Furthermore, the suggested approach identifies the variability in the underlying physiological as well as drug-dependent parameters.
Conclusions: This Bayesian approach showed the potential of uncertainty reduction when predicting diazepam concentrations via integration of the patient’s characteristics and can be extended to predict the pharmacokinetic behaviour of new drugs in different populations.
References:
[1] Greenblatt D J, Allen M D, Harmatz J S and Shader R I. Diazepam disposition determinants. Clin Pharmacol Ther.(1980) 27(3):301-312
[2] Gueorguieva I, Aarons L, Rowland M. Diazepam pharmacokinetics from preclinical to Phase I using a Bayesian population physiological model with informative prior distributions in WINBUGS. J Pharmacokinet Pharmacodyn. (2006) 33(5):571–594
[3] Stan Development Team. 2015. Stan Modeling Language User's Guide and Reference Manual.(2016) Version 2.9.0
