title: 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 μM, 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 μM, 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 (λ/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.
authors.affiliation: Department of Pharmaceutics (J.D.L., N.I.) and Department of Medicinal Chemistry (B.M.V., K.N.B., W.L.N., K.L.K.), School of Pharmacy, University of Washington, Seattle, Washington.
authors.forename: Justin D
authors.initials: JD
authors.lastname: Lutz
authors.forename: Brooke M
authors.initials: BM
authors.lastname: VandenBrink
authors.forename: Katipudi N
authors.initials: KN
authors.lastname: Babu
authors.forename: Wendel L
authors.initials: WL
authors.lastname: Nelson
authors.forename: Kent L
authors.initials: KL
authors.lastname: Kunze
authors.forename: Nina
authors.initials: N
authors.lastname: Isoherranen
mesh_terms: D001189:Aryl Hydrocarbon Hydroxylases / Q000037:antagonists & inhibitors*; D065731:Cytochrome P-450 CYP2C19; D051544:Cytochrome P-450 CYP3A; D065692:Cytochrome P-450 CYP3A Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism; D004347:Drug Interactions / Q000502:physiology; D005473:Fluoxetine / Q000031:analogs & derivatives* / Q000494:pharmacology*; D006801:Humans; D008862:Microsomes, Liver / Q000378:metabolism; D018570:Risk Assessment; D013237:Stereoisomerism
chemical_list: D065692:Cytochrome P-450 CYP3A Inhibitors; D005473:Fluoxetine; D003577:Cytochrome P-450 Enzyme System; D001189:Aryl Hydrocarbon Hydroxylases; C045793:CYP2C19 protein, human; D065731:Cytochrome P-450 CYP2C19; D051544:Cytochrome P-450 CYP3A; C510163:CYP3A4 protein, human; C036139:norfluoxetine