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Cytochrome P450 induction-mediated drug-drug interaction (DDI) is one of the major concerns in clinical practice and for the pharmaceutical industry. Previously, a novel approach [the relative induction score (RIS)] was developed using the Fa2N-4 immortalized human hepatocyte line and proposed as a tool for predicting magnitude of clinical DDIs caused by induction of CYP3A. The approach is based on combining in vitro induction parameters (EC(50) and E(max)) with the efficacious free plasma concentrations to calculate a relative induction score, which is correlated to the magnitude of clinical DDI for midazolam or ethinyl estradiol. To expand the applicability of the RIS model, we have measured induction caused by ten drugs in two different lots of human cryopreserved hepatocytes and correlated the data to clinical DDIs using the RIS. The results demonstrated that, as with Fa2N-4 hepatocytes, sigmoidal relationships can be derived between RIS and magnitude of induction of midazolam and ethinyl estradiol clearance in cryopreserved human hepatocytes. This study demonstrates the general applicability of the relative induction score approach using the human cryopreserved hepatocyte model to predict clinical DDI.

Induction of cytochrome P450 (P450) activity in the clinic can result in therapeutic failure such as tissue rejection in transplant patients or unwanted pregnancy, among others. CYP3A4 is by far the most abundant isoform and is responsible for the majority of P450-related metabolism of all marketed drugs. However, it is of importance to understand the significance of induction mediated through other P450 enzymes. The objective of this investigation was to evaluate several known inducers in vitro using cryopreserved human hepatocytes, with the aim of assessing the relevant induction of CYP3A4, CYP2B6, CYP2C9, CYP2C19, and CYP3A5, based on mRNA expression. CYP3A4 induction was also assessed based on enzymatic activity in three different lots to investigate whether mRNA expression data have any advantages over enzymatic activity. In general, the mRNA fold-induction data results were more sensitive compared with activity data, and more informative in cases in which the drug is also a P450 inhibitor. The induction of transcription of other drug-metabolizing enzymes including CYP2B6 and CYP2C enzymes occurred every time that CYP3A4 mRNA levels increased, but to a lesser extent, indicating that measurement of CYP3A4 mRNA is a sensitive marker for the induction of these other enzymes. This was the case even for enzymes and inducers that are known to also act via the constitutive androstane receptor pathway. Finally, the utility of in vitro induction measurements in the identification of clinically meaningful inducers was tested by using two simple binary classification approaches: 1) fold-induction versus vehicle control and 2) induction response relative to rifampin. The best classification was observed when the cutoff criteria based on fold induction relative to the vehicle control, using mRNA data are used.

Although approaches to the prediction of drug-drug interactions (DDIs) arising via time-dependent inactivation have recently been developed, such approaches do not account for simple competitive inhibition or induction. Accordingly, these approaches do not provide accurate predictions of DDIs arising from simple competitive inhibition (e.g., ketoconazole) or induction of cytochromes P450 (e.g., phenytoin). In addition, methods that focus upon a single interaction mechanism are likely to yield misleading predictions in the face of mixed mechanisms (e.g., ritonavir). As such, we have developed a more comprehensive mathematical model that accounts for the simultaneous influences of competitive inhibition, time-dependent inactivation, and induction of CYP3A in both the liver and intestine to provide a net drug-drug interaction prediction in terms of area under the concentration-time curve ratio. This model provides a framework by which readily obtained in vitro values for competitive inhibition, time-dependent inactivation and induction for the precipitant compound as well as literature values for f(m) and F(G) for the object drug can be used to provide quantitative predictions of DDIs. Using this model, DDIs arising via inactivation (e.g., erythromycin) continue to be well predicted, whereas those arising via competitive inhibition (e.g., ketoconazole), induction (e.g., phenytoin), and mixed mechanisms (e.g., ritonavir) are also predicted within the ranges reported in the clinic. This comprehensive model quantitatively predicts clinical observations with reasonable accuracy and can be a valuable tool to evaluate candidate drugs and rationalize clinical DDIs.