@article {Xu2330, author = {Lilly Xu and Yuping Chen and Yvonne Pan and Gary L. Skiles and Magang Shou}, title = {Prediction of Human Drug-Drug Interactions from Time-Dependent Inactivation of CYP3A4 in Primary Hepatocytes Using a Population-Based Simulator}, volume = {37}, number = {12}, pages = {2330--2339}, year = {2009}, doi = {10.1124/dmd.108.025494}, publisher = {American Society for Pharmacology and Experimental Therapeutics}, abstract = {Time-dependent inactivation (TDI) of human cytochromes P450 3A4 (CYP3A4) is a major cause of clinical drug-drug interactions (DDIs). Human liver microsomes (HLM) are commonly used as an enzyme source for evaluating the inhibition of CYP3A4 by new chemical entities. The inhibition data can then be extrapolated to assess the risk of human DDIs. Using this approach, under- and overpredictions of in vivo DDIs have been observed. In the present study, human hepatocytes were used as an alternative to HLM. Hepatocytes incorporate the effects of other mechanisms of drug metabolism and disposition (i.e., phase II enzymes and transporters) that may modulate the effects of TDI on clinical DDIs. The in vitro potency (KI and kinact) of five known CYP3A4 TDI drugs (clarithromycin, diltiazem, erythromycin, verapamil, and troleandomycin) was determined in HLM (pooled, n = 20) and hepatocytes from two donors (D1 and D2), and the results were extrapolated to predict in vivo DDIs using a Simcyp population trial-based simulator. Compared with observed DDIs, the predictions derived from HLM appeared to be overestimated. The predictions based on TDI measured in hepatocytes were better correlated with the DDIs (n = 37) observed in vivo (R2 = 0.601 for D1 and 0.740 for D2) than those from HLM (R2 = 0.451). In addition, with the use of hepatocytes a greater proportion of the predictions were within a 2-fold range of the clinical DDIs compared with using HLM. These results suggest that DDI predictions from CYP3A4 TDI kinetics in hepatocytes could provide an alternative approach to balance HLM-based predictions that can sometimes substantially overestimate DDIs and possibly lead to erroneous conclusions about clinical risks.Copyright {\textcopyright} 2009 by The American Society for Pharmacology and Experimental Therapeutics}, issn = {0090-9556}, URL = {https://dmd.aspetjournals.org/content/37/12/2330}, eprint = {https://dmd.aspetjournals.org/content/37/12/2330.full.pdf}, journal = {Drug Metabolism and Disposition} }