Time-dependent inhibition (TDI) may confound drug interaction predictions. Recently, models were generated for an array of TDI kinetic schemes using numerical analysis of microsomal assays. Additionally, a distinct terminal inactivation step was identified for certain mechanism based inhibitors (MBI) following reversible metabolite intermediate complex (MIC) formation. Longer hepatocyte incubations potentially allow analysis of slow TDI and terminal inactivation. In the experiments presented here, we compared the quality of TDI parameterization by numerical analysis between hepatocyte and microsomal data. Rat liver microsomes (RLM), suspended rat hepatocytes (SRH) and sandwich-cultured rat hepatocytes (SCRH) were incubated with the prototypical CYP3A MBI troleandomycin and the substrate midazolam. Data from RLM provided a better model fit as compared to SRH. Increased CYP3A expression after dexamethasone (DEX) induction improved the fit for RLM and SRH. A novel sequential kinetic scheme, defining inhibitor metabolite production prior to MIC formation, improved the fit compared to direct MIC formation. Furthermore, terminal inactivation rate constants were parameterized for RLM and SRH samples with DEX-induced CYP3A. The low expression of CYP3A and experimental error in SCRH resulted in poor data for model fitting. Overall, RLM generated data better suited for elucidation of TDI mechanisms by numerical analysis.

时间依赖性抑制 (TDI) 可能会混淆药物相互作用的预测。最近，使用微粒体测定的数值分析为一系列 TDI 动力学方案生成了模型。此外，在可逆代谢物中间体复合物 (MIC) 形成后，针对某些基于机制的抑制剂 (MBI) 确定了不同的终端失活步骤。更长的肝细胞孵化可能允许分析缓慢的 TDI 和终端失活。在此处介绍的实验中，我们通过肝细胞和微粒体数据之间的数值分析比较了 TDI 参数化的质量。大鼠肝微粒体 (RLM)、悬浮大鼠肝细胞 (SRH) 和夹心培养的大鼠肝细胞 (SCRH) 与原型 CYP3A MBI 金霉素和底物咪达唑仑一起孵育。与 SRH 相比，来自 RLM 的数据提供了更好的模型拟合。地塞米松 (DEX) 诱导后 CYP3A 表达增加改善了 RLM 和 SRH 的拟合。一种新的顺序动力学方案，在 MIC 形成之前定义抑制剂代谢物的产生，与直接 MIC 形成相比，改进了拟合。此外，对于具有 DEX 诱导的 CYP3A 的 RLM 和 SRH 样品，终端失活速率常数被参数化。CYP3A 的低表达和 SCRH 中的实验误差导致模型拟合数据不佳。总体而言，RLM 生成的数据更适合通过数值分析阐明 TDI 机制。与直接 MIC 形成相比，改善了拟合。此外，对于具有 DEX 诱导的 CYP3A 的 RLM 和 SRH 样品，终端失活速率常数被参数化。CYP3A 的低表达和 SCRH 中的实验误差导致模型拟合数据不佳。总体而言，RLM 生成的数据更适合通过数值分析阐明 TDI 机制。与直接 MIC 形成相比，改善了拟合。此外，对于具有 DEX 诱导的 CYP3A 的 RLM 和 SRH 样品，终端失活速率常数被参数化。CYP3A 的低表达和 SCRH 中的实验误差导致模型拟合数据不佳。总体而言，RLM 生成的数据更适合通过数值分析阐明 TDI 机制。