Static and Dynamic Projections of Drug-Drug Interactions Caused by Cytochrome P450 3A Time-Dependent Inhibitors Measured in Human Liver Microsomes and Hepatocytes.

Cytochrome P450 3A (CYP3A) is a frequent target for time-dependent inhibition (TDI) that can give rise to drug-drug interactions (DDI). Yet many drugs that exhibit in vitro TDI for CYP3A, do not result in DDI. Twenty-three drugs with published clinical DDI were evaluated for CYP3A TDI in human liver microsomes (HLM) and hepatocytes (HHEP), and these data were utilized in static and dynamic models for projecting DDI caused by inactivation of CYP3A in both liver and intestine. TDI parameters measured in HHEP, particularly kinact, were generally lower than those measured in HLM. In static models, the use of average unbound organ exit concentrations offered the most accurate projections of DDI with geometric mean fold errors of 2.2 and 1.7 for HLM and HHEP, respectively. Use of maximum organ entry concentrations yielded marked overestimates of DDI. When evaluated in a binary fashion (i.e. projection of DDI of 1.25-fold or greater), data from HLM offered the greatest sensitivity (100%) and specificity (42%) and yielded no missed DDI when average unbound organ exit concentrations were used. In dynamic physiologically-based pharmacokinetic modeling, accurate projections of DDI were obtained with geometric mean fold errors of 1.7 and 1.6 for HLM and HHEP, respectively. Sensitivity and specificity were 100% and 67% when using TDI data generated in HLM and Simcyp modeling. Overall, DDI caused by CYP3A-mediated TDI can be reliably projected using dynamic or static models. For static models, average organ unbound exit concentrations must be used as input values otherwise DDI will be markedly overestimated. Significance Statement CYP3A time-dependent inhibitors are important in design and development of new drugs. The prevalence of CYP3A TDI is high among newly synthesized drug candidates and understanding the potential need for running clinical DDI studies is essential during drug development. Ability to reliably predict DDI caused by CYP3A TDI has been difficult to achieve. We report a thorough evaluation of CYP3A TDI and demonstrate that DDI can be predicted when using appropriate models and input parameters generated in HLM or HHEP.