RT Journal Article
SR Electronic
T1 Contribution of Major Metabolites toward Complex Drug-Drug Interactions of Deleobuvir: In Vitro Predictions and In Vivo Outcomes
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP 466
OP 475
DO 10.1124/dmd.115.066985
VO 44
IS 3
A1 Sane, Rucha S.
A1 Ramsden, Diane
A1 Sabo, John P.
A1 Cooper, Curtis
A1 Rowland, Lois
A1 Ting, Naitee
A1 Whitcher-Johnstone, Andrea
A1 Tweedie, Donald J.
YR 2016
UL http://dmd.aspetjournals.org/content/44/3/466.abstract
AB The drug-drug interaction (DDI) potential of deleobuvir, an hepatitis C virus (HCV) polymerase inhibitor, and its two major metabolites, CD 6168 (formed via reduction by gut bacteria) and deleobuvir-acyl glucuronide (AG), was assessed in vitro. Area-under-the-curve (AUC) ratios (AUCi/AUC) were predicted using a static model and compared with actual AUC ratios for probe substrates in a P450 cocktail of caffeine (CYP1A2), tolbutamide (CYP2C9), and midazolam (CYP3A4), administered before and after 8 days of deleobuvir administration to HCV-infected patients. In vitro studies assessed inhibition, inactivation and induction of P450s. Induction was assessed in a short-incubation (10 hours) hepatocyte assay, validated using positive controls, to circumvent cytotoxicity seen with deleobuvir and its metabolites. Overall, P450 isoforms were differentially affected by deleobuvir and its two metabolites. Of note was more potent CYP2C8 inactivation by deleobuvir-AG than deleobuvir and P450 induction by CD 6168 but not by deleobuvir. The predicted net AUC ratios for probe substrates were 2.92 (CYP1A2), 0.45 (CYP2C9), and 0.97 (CYP3A4) compared with clinically observed ratios of 1.64 (CYP1A2), 0.86 (CYP2C9), and 1.23 (CYP3A4). Predictions of DDI using deleobuvir alone would have significantly over-predicted the DDI potential for CYP3A4 inhibition (AUC ratio of 6.15). Including metabolite data brought the predicted net effect close to the observed DDI. However, the static model over-predicted the induction of CYP2C9 and inhibition/inactivation of CYP1A2. This multiple-perpetrator DDI scenario highlights the application of the static model for predicting complex DDI for CYP3A4 and exemplifies the importance of including key metabolites in an overall DDI assessment.