PT  - JOURNAL ARTICLE
AU  - Ying-Hong Wang
AU  - David R. Jones
AU  - Stephen D. Hall
TI  - PREDICTION OF CYTOCHROME P450 3A INHIBITION BY VERAPAMIL ENANTIOMERS AND THEIR METABOLITES
AID  - 10.1124/dmd.32.2.259
DP  - 2004 Feb 01
TA  - Drug Metabolism and Disposition
PG  - 259--266
VI  - 32
IP  - 2
4099  - http://dmd.aspetjournals.org/content/32/2/259.short
4100  - http://dmd.aspetjournals.org/content/32/2/259.full
SO  - Drug Metab Dispos2004 Feb 01; 32
AB  - Verapamil inhibition of CYP3A activity results in many drug-drug interactions with CYP3A substrates, but the mechanism of inhibition is unclear. The present study showed that verapamil enantiomers and their major metabolites [norverapamil and N-desalkylverapamil (D617)] inhibited CYP3A in a time- and concentration-dependent manner by using pooled human liver microsomes and the cDNA-expressed CYP3A4 (+b5). The values of the inactivation kinetic parameters kinact and KI obtained with the cDNA-expressed CYP3A4 (+b5) were 0.39 min-1 and 6.46 μM for R-verapamil, 0.64 min-1 and 2.97 μM for S-verapamil, 1.12 min-1 and 5.89 μM for (±)-norverapamil, and 0.07 min-1 and 7.93 μM for D617. Based on the ratio of kinact and KI, the inactivation potency of verapamil enantiomers and their metabolites was in the following order: S-norverapamil > S-verapamil > R-norverapamil > R-verapamil > D617. Using dual beam spectrophotometry, we confirmed that metabolic intermediate complex formation with CYP3A was the mechanism of inactivation for all compounds. The in vitro unbound fraction was 0.84 for S-verapamil, 0.68 for R-verapamil, and 0.84 for (±)-norverapamil. A mechanism-based pharmacokinetic model predicted that the oral area under the curve (AUC) of a CYP3A substrate that is eliminated completely (fm = 1) by the hepatic CYP3A increased 1.6- to 2.2-fold after repeated oral administration of verapamil. For midazolam (fm = 0.9), a drug that undergoes extensive intestinal wall metabolism, the predicted increase in oral AUC was 3.2- to 4.5-fold. The predicted results correlate well with the in vivo drug interaction data, suggesting that the model is suitable for predicting drug interactions by mechanism-based inhibitors. The American Society for Pharmacology and Experimental Therapeutics