Unknown

Dataset Information

0

Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems.


ABSTRACT: Recent guidance on drug-drug interaction (DDI) testing recommends evaluation of circulating metabolites. However, there is little consensus on how to quantitatively predict and/or assess the risk of in vivo DDIs by multiple time-dependent inhibitors (TDIs) including metabolites from in vitro data. Fluoxetine was chosen as the model drug to evaluate the role of TDI metabolites in DDI prediction because it is a TDI of both CYP3A4 and CYP2C19 with a circulating N-dealkylated inhibitory metabolite, norfluoxetine. In pooled human liver microsomes, both enantiomers of fluoxetine and norfluoxetine were TDIs of CYP2C19, (S)-norfluoxetine was the most potent inhibitor with time-dependent inhibition affinity constant (KI) of 7 ?M, and apparent maximum time-dependent inhibition rate (k(inact,app)) of 0.059 min(-1). Only (S)-fluoxetine and (R)-norfluoxetine were TDIs of CYP3A4, with (R)-norfluoxetine being the most potent (K(I) = 8 ?M, and k(inact,app) = 0.011 min(-1)). Based on in-vitro-to-in-vivo predictions, (S)-norfluoxetine plays the most important role in in vivo CYP2C19 DDIs, whereas (R)-norfluoxetine is most important in CYP3A4 DDIs. Comparison of two multiple TDI prediction models demonstrated significant differences between them in in-vitro-to-in-vitro predictions but not in in-vitro-to-in-vivo predictions. Inclusion of all four inhibitors predicted an in vivo decrease in CYP2C19 (95%) and CYP3A4 (60-62%) activity. The results of this study suggest that adequate worst-case risk assessment for in vivo DDIs by multiple TDI systems can be achieved by incorporating time-dependent inhibition by both parent and metabolite via simple addition of the in vivo time-dependent inhibition rate/cytochrome P450 degradation rate constant (?/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.

SUBMITTER: Lutz JD 

PROVIDER: S-EPMC3834134 | BioStudies | 2013-01-01T00:00:00Z

REPOSITORIES: biostudies

Similar Datasets

2013-01-01 | S-EPMC3684819 | BioStudies
2014-01-01 | S-EPMC4029899 | BioStudies
2020-01-01 | S-EPMC7720126 | BioStudies
2018-01-01 | S-EPMC5938745 | BioStudies
2018-01-01 | S-EPMC5980543 | BioStudies
2020-01-01 | S-EPMC7515950 | BioStudies
2021-01-01 | S-EPMC7825188 | BioStudies
2018-01-01 | S-EPMC5786304 | BioStudies
2020-01-01 | S-EPMC6959988 | BioStudies
1000-01-01 | S-EPMC5911691 | BioStudies