RT Journal Article
SR Electronic
T1 Stereoselective Inhibition of CYP2C19 and CYP3A4 by Fluoxetine and Its Metabolite: Implications for Risk Assessment of Multiple Time-Dependent Inhibitor Systems
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP 2056
OP 2065
DO 10.1124/dmd.113.052639
VO 41
IS 12
A1 Justin D. Lutz
A1 Brooke M. VandenBrink
A1 Katipudi N. Babu
A1 Wendel L. Nelson
A1 Kent L. Kunze
A1 Nina Isoherranen
YR 2013
UL http://dmd.aspetjournals.org/content/41/12/2056.abstract
AB 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 μM, and apparent maximum time-dependent inhibition rate (kinact,app) of 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 the in vivo time-dependent inhibition rate/cytochrome P450 degradation rate constant (λ/kdeg) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.