Time-dependent inhibition (TDI) of cytochrome P450 enzymes is an important cause of drug-drug interactions. The standard approach to characterize the kinetics of TDI is to determine the rate of enzyme loss, k obs , at various inhibitor concentrations, [I], and replot the k obs versus [I] to obtain the key kinetic parameters, K I and k inact . In our companion manuscript (Part 1; Nagar et al., 2014) in this issue of Drug Metabolism and Disposition, we used simulated datasets to develop and test a new numerical method to analyze in vitro TDI data. Here, we have applied this numerical method to five TDI datasets. Experimental datasets include the inactivation of CYP2B6, CYP2C8, and CYP3A4. None of the datasets exhibited Michaelis-Menten-only kinetics, and the numerical method allowed use of more complex models to fit each dataset. Quasi-irreversible as well as partial inhibition kinetics were observed and parameterized. Three datasets required the use of a multiple-inhibitor binding model. The mechanistic and clinical implications provided by these analyses are discussed. Together with the results in Part 1, we have developed and applied a new numerical method for analysis of in vitro TDI data. This method appears to be generally applicable to model in vitro TDI data with atypical and complex kinetic schemes.

Deleobuvir is a potent inhibitor of the hepatitis C virus nonstructural protein 5B polymerase. In humans, deleobuvir underwent extensive reduction to form CD 6168. This metabolite was not formed in vitro in aerobic incubations with human liver microsomes or cytosol. Anaerobic incubations of deleobuvir with rat and human fecal homogenates produced CD 6168. Using these in vitro formation rates, a retrospective analysis was conducted to assess whether the fecal formation of CD 6168 could account for the in vivo levels of this metabolite. The formation of CD 6168 was also investigated using a pseudo-germ free (pGF) rat model, in which gut microbiota were largely eradicated by antibiotic treatment. Plasma exposure (area under the curve from 0 to ') of CD 6168 was approximately 9-fold lower in pGF rats (146 6 64 ng·h/ml) compared with control rats (1,312 6 649 ng·h/ml). Similarly, in pGF rats, lower levels of CD 6168 (1.5% of the deleobuvir dose) were excreted in feces compared with control rats (42% of the deleobuvir dose). In agreement with these findings, in pGF rats, approximately all of the deleobuvir dose was excreted as deleobuvir into feces (105% of dose), whereas only 26% of the deleobuvir dose was excreted as deleobuvir in control rats. These differences in plasma and excretion profiles between pGF and control rats confirm the role of gut bacteria in the formation of CD 6168. These results underline the importance of evaluating metabolism by gut bacteria and highlight experimental approaches for nonclinical assessment of bacterial metabolism in drug development.

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.

18 metabolites was observed. Their structures were proposed on the basis of high-performance liquid chromatography-tandem mass spectrometry technologies, and 10 metabolites were confirmed by comparison with synthetic standards. We were surprised to find that a disproportionate human metabolite, BILR 516, was uncovered during this metabolite profiling study and pharmacokinetic analysis of BILR 516 showed that it had a longer half-life and higher exposure than the parent compound at steady state. Of interest, BILR 516 was not detected in human plasma when BILR 355 was administered alone. Therefore, whereas RTV boosted the exposure of BILR 355, it resulted in a significant metabolic switching of BILR 355. Overall, this article demonstrates an unusual example of metabolic switching and raises concern about the consequence of metabolic switching during drug development.