Quantitative Prediction and Clinical Observation of a CYP3A Inhibitor-Based Drug-Drug Interactions with MLN3897, a Potent C-C Chemokine Receptor-1 Antagonist
ABSTRACT A novel in vitro model was recently developed in our laboratories for the prediction of magnitude of clinical pharmacokinetic drugdrug interactions (DDIs), based on reversible hepatic cytochrome P450 (P450) inhibition. This approach, using inhibition data from human hepatocytes incubated in human plasma, and quantitative P450 phenotyping data from hepatic microsomal incubations, successfully predicted DDIs for 15 marketed drugs with ketoconazole, a strong competitive inhibitor of CYP3A4/5, generally used to demonstrate a "worst-case scenario" for CYP3A inhibition. In addition, this approach was successfully extended to DDI predictions with the moderate competitive CYP3A inhibitor fluconazole for nine marketed drugs. In the current report, the general applicability of the model has been demonstrated by prospectively predicting the degree of inhibition and then conducting DDI studies in the clinic for an investigational CCR1 antagonist MLN3897, which is cleared predominantly by CYP3A. The clinical studies involved treatment of healthy volunteers (n ϭ 17-20), in a crossover design, with ketoconazole (200 mg b.i.d.) or fluconazole (400 mg once a day), while receiving MLN3897. Administration of MLN3897 and ketoconazole led to an average 8.28-fold increase in area under the curve of plasma concentration-time plot (AUC) of MLN3897 at steady state, compared with the 8.33-fold increase predicted from the in vitro data. Similarly for fluconazole, an average increase of 3.93-fold in AUC was observed for MLN3897 in comparison with a predicted value of 3.26-fold. Thus, our model reliably predicted the exposure changes for MLN3897 in interaction studies with competitive CYP3A inhibitors in humans, further strengthening the utility of our in vitro model. Prediction of clinical DDIs using in vitro studies is one of the major challenges in the pharmaceutical industry. The main utility of such DDI predictions is to help foresee any safety issues anticipated from higher exposures and thus help design clinical trials with better safety. In some instances, clinical DDI studies can be avoided if no significant pharmacokinetic interaction is predicted. Traditionally, DDI predictions have been based on the ratio of the inhibitor concentration [I] and the enzyme inhibition constant K i ([I]/K i ratio
