Mechanism-Based Inactivation of Human Cytochrome P 450 Enzymes and the Prediction of Drug-Drug Interactions

The ability to use vitro inactivation kinetic parameters in scaling to in vivo drug-drug interactions (DDIs) for mechanism-based inactivators of human cytochrome P450 (P450) enzymes was examined using eight human P450-selective marker activities in pooled human liver microsomes. These data were combined with other parameters (systemic Cmax, estimated hepatic inlet Cmax, fraction unbound, in vivo P450 enzyme degradation rate constants estimated from clinical pharmacokinetic data, and fraction of the affected drug cleared by the inhibited enzyme) to predict increases in exposure to drugs, and the predictions were compared with in vivo DDIs gathered from clinical studies reported in the scientific literature. In general, the use of unbound systemic Cmax as the inactivator concentration in vivo yielded the most accurate predictions of DDI with a mean -fold error of 1.64. Abbreviated in vitro approaches to identifying mechanism-based inactivators were developed. Testing potential inactivators at a single concentration (IC25) in a 30-min preincubation with human liver microsomes in the absence and presence of NADPH followed by assessment of P450 marker activities readily identified those compounds known to be mechanism-based inactivators and represents an approach that can be used with greater throughput. Measurement of decreases in IC50 occurring with a 30-min preincubation with liver microsomes and NADPH was also useful in identifying mechanismbased inactivators, and the IC50 measured after such a preincubation was highly correlated with the kinact/KI ratio measured after a full characterization of inactivation. Overall, these findings support the conclusion that P450 in vitro inactivation data are valuable in predicting clinical DDIs that can occur via this mechanism. The prediction of drug-drug interactions (DDIs) using in vitro enzyme kinetic data has been an area of increasing advances and sophistication. This has proven to be a valuable endeavor because DDIs remain an important issue in clinical practice and the discovery and development of new drugs. The earlier that the potential for DDIs can be identified in new compounds being studied as potential drugs, the greater the likelihood that this deleterious property can be removed through improved design of the molecule. Also, for those compounds already undergoing clinical trials, in vitro DDI data can be leveraged in the design of adequate and appropriate clinical DDI studies. With our increased understanding of drug-metabolizing enzymes and their roles in the metabolism of specific drugs, a mechanistic approach to assessing DDIs can be taken. The results of clinical DDI studies with one drug can be extrapolated to other drugs that are cleared by the same enzyme. The alteration of drug-metabolizing enzyme activities can occur by three main mechanisms: reversible inhibition, mechanism-based inactivation, and induction. Confidence in quantitatively extrapolating in vitro results to in vivo varies with these mechanisms. For reversible inhibition mechanisms, recent advances in our ability to predict the magnitude of DDIs from in vitro inhibition data have been made such that for cytochrome P450 (P450) enzymes, increases in exposure can be predicted within 2-fold (Brown et al., 2005; Ito et al., 2005; Obach et al., 2006). Through the use of human hepatocytes in culture, enzyme inducers can be readily identified (Silva and Nicholl-Griffith, 2002). However, the magnitude of predicted DDIs for inducers varies with the source of individual hepatocytes. Recently, in vitro induction data from an immortalized human hepatocyte line, which gives a robust and reliable response, has been shown to yield quantitative predictions of CYP3A induction-based DDIs in vivo (Ripp et al.,