Inter-phenotypic differences in CYP2C9 and CYP2C19 metabolism: Bayesian meta-regression of human population variability in kinetics and application in chemical risk assessment

Abstract

Quantifying variability in pharmacokinetics (PK) and toxicokinetics (TK) provides a science-based approach to refine uncertainty factors (UFs) for chemical risk assessment. In this context, genetic polymorphisms in cytochromes P450 (CYPs) drive inter-phenotypic differences and may result in reduction or increase in metabolism of drugs or other xenobiotics. Here, an extensive literature search was performed to identify PK data for probe substrates of the human polymorphic isoforms CYP2C9 and CYP2C19. Relevant data from 158 publications were extracted for markers of chronic exposure (clearance and area under the plasma concentration-time curve) and analysed using a Bayesian meta-regression model. Enzyme function (EF), driven by inter-phenotypic differences across a range of allozymes present in extensive and poor metabolisers (EMs and PMs), and fraction metabolised (Fm), were identified as exhibiting the highest impact on the metabolism. The Bayesian meta-regression model provided good predictions for such inter-phenotypic differences. Integration of population distributions for inter-phenotypic differences and estimates for EF and Fm allowed the derivation of CYP2C9- and CYP2C19-related UFs which ranged from 2.7 to 12.7, and were above the default factor for human variability in TK (3.16) for PMs and major substrates (Fm >60%). These results provide population distributions and pathway-related UFs as conservative in silico options to integrate variability in CYP2C9 and CYP2C19 metabolism using in vitro kinetic evidence and in the absence of human data. The future development of quantitative extrapolation models is discussed with particular attention to integrating human in vitro and in vivo PK or TK data with pathway-related variability for chemical risk assessment.