S-pantoprazole sodium

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.

Drug-drug interactions (DDI) and genomic variation (PG) can lead to dangerously high blood and tissue concentrations with some drugs but may be negligible with other drugs. Using a quantitative metaanalysis, we analyzed on the example of CYP2D6 and CYP2C19 substrates, how well the effects of DDI and PG can be predicted by in vitro methods. In addition, we analyzed the quantitative effect of prototypic inhibitors of the two enzymes in relation to their genetic deficiency. More than 600 published studies were screened which compared either human pharmacokinetics with and without comedication, or which compared human pharmacokinetics of deficient with extensive metabolizers, or which assessed metabolism by in vitro approaches. With human liver microsomes, the in vitro to in vivo agreement of fractional clearances was reasonably high if loss of substrate was quantified in the in vitro assays performed with and without enzyme specific inhibitors. Also a generally very high correlation between the clinical pharmacokinetic effects of inherited deficiency and inhibition by drug-drug interactions could be demonstrated. Most cases of poor correlation were explained by the lack of CYP2D6 versus CYP2C19 specificity of fluoxetine or by a poor knowledge of the quantitative contribution of the metabolic pathways if metabolite formation was quantified in the in vitro assays. The good correspondence of the in vitro data with clinical DDI and clinical PG studies may be a good basis for future application of these methods in drug development and drug therapy.