Inhibition of CYP2C19 and CYP3A4 by omeprazole metabolites and their contribution to drug-drug interactions

Abstract

The aim of this study was to evaluate the contribution of metabolites to drug-drug interactions (DDI) using the inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites as a model. Of the metabolites identified in vivo, 5-hydroxyomeprazole, 5'-O-desmethylomeprazole, omeprazole sulfone, and carboxyomeprazole had a metabolite to parent area under the plasma concentration-time curve (AUC(m)/AUC(p)) ratio ≥ 0.25 when either total or unbound concentrations were measured after a single 20-mg dose of omeprazole in a cocktail. All of the metabolites inhibited CYP2C19 and CYP3A4 reversibly. In addition omeprazole, omeprazole sulfone, and 5'-O-desmethylomeprazole were time dependent inhibitors (TDI) of CYP2C19, whereas omeprazole and 5'-O-desmethylomeprazole were found to be TDIs of CYP3A4. The in vitro inhibition constants and in vivo plasma concentrations were used to evaluate whether characterization of the metabolites affected DDI risk assessment. Identifying omeprazole as a TDI of both CYP2C19 and CYP3A4 was the most important factor in DDI risk assessment. Consideration of reversible inhibition by omeprazole and its metabolites would not identify DDI risk with CYP3A4, and with CYP2C19, reversible inhibition values would only identify DDI risk if the metabolites were included in the assessment. On the basis of inactivation data, CYP2C19 and CYP3A4 inhibition by omeprazole would be sufficient to identify risk, but metabolites were predicted to contribute 30-63% to the in vivo hepatic interactions. Therefore, consideration of metabolites may be important in quantitative predictions of in vivo DDIs. The results of this study show that, although metabolites contribute to in vivo DDIs, their relative abundance in circulation or logP values do not predict their contribution to in vivo DDI risk.

Fig. 1.

Mean plasma concentration–time profiles of omeprazole and its metabolites in nine healthy volunteers after oral administration of 20 mg omeprazole. Plasma concentrations of (A) omeprazole, (B) 5-hydroxyomeprazole, (C) 5′-O-desmethylomeprazole, (D) omeprazole sulfone, and (E) carboxyomeprazole were quantified. Data are shown as means ± S.D. (n = 9).

Fig. 2.

NADPH-dependent IC₅₀ shifts for OMP and its metabolites for CYP2C19-catalyzed (S)-mephenytoin hydroxylation and CYP3A4-catalyzed midazolam hydroxylation in HLMs. Inhibition of CYP2C19 and CYP3A4 by OMP (A and B), 5′-O-desmethylomeprazole (C and D), and OMP sulfone (E and F) is shown after a 30-minute preincubation with the inhibitor in the presence or absence of NADPH. All incubations were done as described in Materials and Methods. Data are shown as means ± S.D. (n = 3).

Fig. 3.

Time dependent inhibition kinetics of CYP2C19 by OMP (A), 5′-O-desmethylomeprazole (B), and OMP sulfone (C) in HLMs using (S)-mephenytoin hydroxylation as a probe. The left panels show the time-dependent inhibition of CYP2C19 at various concentrations of OMP and its metabolites. The right panels show the fit of Eq. 2 to the data. Data are shown as means ± S.D. (n = 3).

Fig. 4.

Time dependent inhibition kinetics of CYP3A4 by OMP in HLM (A), and CYP3A4 supersomes (B), and 5′-O-desmethylomeprazole in HLMs (C). The left panels show the time-dependent inhibition of CYP3A4 at various concentrations of OMP and DM-OMP. The right panels show the fit of Eq. 2 to the data. Data are shown as means ± S.D. (n = 3).

Fig. 5.

Predicted relative contribution of OMP and its metabolites to reversible (I/IC₅₀) and irreversible (λ/k_deg) CYP2C19 and CYP3A4 inhibition. The inhibition risk was predicted using unbound C_max values.