Wendling, Thierry, Dumitras, Swati, Woessner, Ralph, Pigeolet, Etienne, Ogungbenro, Kayode and Aarons, Leon (2015) Application of a Bayesian population approach to physiological modelling of mavoglurant pharmacokinetics. Journal of Pharmacokinetics and Pharmacodynamics.

Abstract

Mavoglurant (MVG) is an antagonist at the metabotropic glutamate receptor-5 currently under clinical development at Novartis Pharma AG for the treatment of central nervous system diseases. The aim of this study was to develop and optimise a population whole-body physiologically-based pharmacokinetic (WBPBPK) model for MVG, to predict the impact of drug-drug interaction (DDI) and age on its pharmacokinetics. In a first step, the model was fitted to intravenous (IV) data from a Phase-I clinical study in adults using a Bayesian approach. In a second step, the optimised model was used together with a mechanistic absorption model for exploratory Monte Carlo simulations. The ability of the model to predict MVG pharmacokinetics when orally co-administered with ketoconazole in adults or administered alone in 3 to 11 year-old children was evaluated using data from three other clinical studies. The population model allowed good description of both the median trend and inter-individual variability in MVG plasma pharmacokinetics following IV administration in adults. The Bayesian approach allowed uncertainty in some parameters to be reduced. Prediction of the DDI with ketoconazole was consistent with the results of a non-compartmental analysis of the clinical data (3-fold increase in systemic exposure). Scaling of the WBPBPK model allowed reasonable extrapolation of MVG pharmacokinetics from adults to children. The model could be used to predict plasma and brain (target site) concentration-time profiles following oral administration of various immediate-release formulations of MVG alone or when co-administered with other drugs, in adults as well as in children.

Item Type:	Article
Date Deposited:	13 Oct 2015 13:11
Last Modified:	13 Oct 2015 13:11
URI:	https://oak.novartis.com/id/eprint/25851