- Time-dependent inhibition and estimation of CYP3A clinical pharmacokinetic drug-drug interactions using plated human cell systems.
Time-dependent inhibition and estimation of CYP3A clinical pharmacokinetic drug-drug interactions using plated human cell systems.
The current studies assessed the utility of freshly plated hepatocytes, cryopreserved plated hepatocytes, and cryopreserved plated HepaRG cells for the estimation of inactivation parameters k(inact) and K(I) for CYP3A. This was achieved using a subset of CYP3A time-dependent inhibitors (fluoxetine, verapamil, clarithromycin, troleandomycin, and mibefradil) representing a range of potencies. The estimated k(inact) and K(I) values for each time-dependent inhibitor were compared with those obtained using human liver microsomes and used to estimate the magnitude of clinical pharmacokinetic drug-drug interaction (DDI). The inactivation kinetic parameter, k(inact), was most consistent across systems tested for clarithromycin, verapamil, and troleandomycin, with a high k(inact) of 0.91 min(-1) observed for mibefradil in HepaRG cells. The apparent K(I) estimates derived from the various systems displayed a range of variability from 3-fold for clarithromycin (5.4-17.7 μM) to 6-fold for verapamil (1.9-12.6 μM). In general, the inactivation kinetic parameters derived from the cell systems tested fairly replicated what was observed in time-dependent inhibition studies using human liver microsomes. Despite some of the observed differences in inactivation kinetic parameters, the estimated DDIs derived from each of the tested systems generally agreed with the clinically reported DDI within approximately 2-fold. In addition, a plated cell approach offered the ability to conduct longer primary incubations (greater than 30 min), which afforded improved ability to identify the weak time-dependent inhibitor fluoxetine. Overall, results from these studies suggest that in vitro inactivation parameters generated from plated cell systems may be a practical approach for identifying time-dependent inhibitors and for estimating the magnitude of clinical DDIs.