Modeling, Prediction, and in Vitro in Vivo Correlation of CYP3A4 Induction

CYP3A4 induction is not generally considered to be a concern for safety; however, serious therapeutic failures can occur with drugs whose exposure is lower as a result of more rapid metabolic clearance due to induction. Despite the potential therapeutic consequences of induction, little progress has been made in quantitative predictions of CYP3A4 induction-mediated drug-drug interactions (DDIs) from in vitro data. In the present study, predictive models have been developed to facilitate extrapolation of CYP3A4 induction measured in vitro to human clinical DDIs. The following parameters were incorporated into the DDI predictions: 1) EC₅₀ and E_max of CYP3A4 induction in primary hepatocytes; 2) fractions unbound of the inducers in human plasma (f_{u, p}) and hepatocytes (f_{u, hept}); 3) relevant clinical in vivo concentrations of the inducers ([Ind]_{max, ss}); and 4) fractions of the victim drugs cleared by CYP3A4 (f_{m, CYP3A4}). The values for [Ind]_{max, ss} and f_{m, CYP3A4} were obtained from clinical reports of CYP3A4 induction and inhibition, respectively. Exposure differences of the affected drugs in the presence and absence of the six individual inducers (bosentan, carbamazepine, dexamethasone, efavirenz, phenobarbital, and rifampicin) were predicted from the in vitro data and then correlated with those reported clinically (n = 103). The best correlation was observed (R² = 0.624 and 0.578 from two hepatocyte donors) when f_{u, p} and f_{u, hept} were included in the predictions. Factors that could cause over- or underpredictions (potential outliers) of the DDIs were also analyzed. Collectively, these predictive models could add value to the assessment of risks associated with CYP3A4 induction-based DDIs by enabling their determination in the early stages of drug development.