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Stereoselective Inhibition of CYP2C19 and CYP3A4 by Fluoxetine and its Metabolite: Implications For Risk Assessment of Multiple Time-dependent Inhibitor Systems.

Drug Metab Dispos. 2013 Jun 19;

Authors: Lutz JD, Vandenbrink BM, Babu NK, Nelson WL, Kunze KL, Isoherranen N


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 HLMs, both enantiomers of fluoxetine and norfluoxetine were TDIs of CYP2C19, with (S)-norfluoxetine being the most potent (KI = 7 μM and kinact,app = 0.059 min(-1)). Only (S)-fluoxetine and (R)-norfluoxetine were TDIs of CYP3A4, with (R)-norfluoxetine being the most potent (KI = 8 μM and kinact,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 in vivo λ/kdeg values, but quantitative DDI predictions will require more thorough understanding of TDI mechanisms.

PMID: 23785064 [PubMed - as supplied by publisher]