In vivo information-guided prediction approach for assessing the risks of drug-drug interactions associated with circulating inhibitory metabolites

Abstract

The in vivo drug-drug interaction (DDI) risks associated with cytochrome P450 inhibitors that have circulating inhibitory metabolites cannot be accurately predicted by conventional in vitro-based methods. A novel approach, in vivo information-guided prediction (IVIP), was recently introduced for CYP3A- and CYP2D6-mediated DDIs. This technique should be applicable to the prediction of DDIs involving other important cytochrome P450 metabolic pathways. Therefore, the aims of this study were to extend the IVIP approach to CYP2C9-mediated DDIs and evaluate the IVIP approach for predicting DDIs associated with inhibitory metabolites. The analysis was based on data from reported DDIs in the literature. The IVIP approach was modified and extended to CYP2C9-mediated DDIs. Thereafter, the IVIP approach was evaluated for predicting the DDI risks of various inhibitors with inhibitory metabolites. Although the data on CYP2C9-mediated DDIs were limited compared with those for CYP3A- and CYP2D6-mediated DDIs, the modified IVIP approach successfully predicted CYP2C9-mediated DDIs. For the external validation set, the prediction accuracy for area under the plasma concentration-time curve (AUC) ratios ranged from 70 to 125%. The accuracy (75-128%) of the IVIP approach in predicting DDI risks of inhibitors with circulating inhibitory metabolites was more accurate than in vitro-based methods (28-805%). The IVIP model accommodates important confounding factors in the prediction of DDIs, which are difficult to handle using in vitro-based methods. In conclusion, the IVIP approach could be used to predict CYP2C9-mediated DDIs and is easily modified to incorporate the additive effect of circulating inhibitory metabolites.

Fig. 1.

Predicted versus observed AUC_I/AUC ratios (CYP2C9) in the external validation set (A; the dotted red lines represent 50–200% ranges of the prediction accuracy), prediction accuracy for the external validation set (B), and predicted AUC_I/AUC of substrates in the presence of various inhibitors (C). In C, the magnitude of the DDI increased from yellow to red; the doses for inhibitors amiodarone, fluconazole, and fluvoxamine are 400, 400, and 150 mg/day, respectively. The doses for other inhibitors are shown in Table 2. LOS, losartan; DIC, diclofenac; IBU, S-ibuprofen; MEL, meloxicam; ZAF, zafirlukast; FLT, fluvastatin; FLP, flurbiprofen; ETR, etravirine; WAR, S-warfarin; PHE, phenytoin; TOL, tolbutamide; GLI, glimepiride; PAR, paroxetine; DIL, diltiazem; SER, sertraline; CIM, cimetidine; FLX, fluvoxamine; SUR, sulfinpyrazone; KET, ketoconazole; VOR, voriconazole; AMI, amiod arone; BEN, benzbromarone; SUN, sulfaphenazole; FLN, fluconazole; BUC, bucolome; MIC, miconazole.

Fig. 2.

Predicted versus observed AUC_I/AUC ratios for in vivo drug-drug interaction studies associated with inhibitors that have inhibitory metabolites. The prediction was based on four different models. The dotted red lines represent 50 to 200% ranges of the prediction accuracy. For the inhibitors diltiazem and erythromycin, only the predicted AUC ratios based on the irreversible inhibition model were shown. In vitro (P), only the data of the parent drugs were included in the in vitro-based prediction approach and total systemic C_max was used to estimate the perpetrator concentration available to the enzyme; in vitro (P+M), total systemic C_max for the parent drug and metabolites were used in the in vitro-based prediction approach; in vitro UHI (P+M), unbound hepatic inlet concentrations of parent drug and metabolites were used to estimate the perpetrator concentration available to the enzyme.