Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania
Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, Pennsylvania.
Drug Metab Dispos. 2024 Sep 16;52(10):1060-1072. doi: 10.1124/dmd.124.001782.
One-compartment (1C) and permeability-limited models were used to evaluate the ability of microsomal and hepatocyte intrinsic clearances to predict hepatic clearance. Well-stirred (WSM), parallel-tube (PTM), and dispersion (DM) models were evaluated within the liver as well as within whole-body physiologically based pharmacokinetic frameworks. It was shown that a linear combination of well-stirred and parallel-tube average liver blood concentrations accurately approximates dispersion model blood concentrations. Using a flow/permeability-limited model, a large systematic error was observed for acids and no systematic error for bases. A scaling factor that reduced interstitial fluid (ISF) plasma protein binding could greatly decrease the absolute average fold error (AAFE) for acids. Using a 1C model, a scalar to reduce plasma protein binding decreased the microsomal clearance AAFE for both acids and bases. With a permeability-limited model, only acids required this scalar. The mechanism of the apparent increased cytosolic concentrations for acids remains unknown. We also show that for hepatocyte intrinsic clearance in vitro-in vivo correlations (IVIVCs), a 1C model is mechanistically appropriate since hepatocyte clearance should represent the net clearance from ISF to elimination. A relationship was derived that uses microsomal and hepatocyte intrinsic clearance to solve for an active hepatic uptake clearance, but the results were inconclusive. Finally, the PTM model generally performed better than the WSM or DM models, with no clear advantage between microsomes and hepatocytes. SIGNIFICANCE STATEMENT: Prediction of drug clearance from microsomes or hepatocytes remains challenging. Various liver models (e.g., well-stirred, parallel-tube, and dispersion) have been mathematically incorporated into liver as well as whole-body physiologically based pharmacokinetic frameworks. Although the resulting models allow incorporation of pH partitioning, permeability, and active uptake for prediction of drug clearance, including these processes did not improve clearance predictions for both microsomes and hepatocytes.
单室(1C)和渗透限制模型用于评估微粒体和肝细胞固有清除率预测肝清除率的能力。在肝脏内以及整个全身生理基础药代动力学框架内评估了完全混合(WSM)、平行管(PTM)和弥散(DM)模型。结果表明,平均肝血浓度的线性组合可以很好地近似弥散模型的血浓度。使用流动/渗透限制模型,观察到酸的线性组合存在较大的系统误差,而碱则没有系统误差。降低间质液(ISF)中血浆蛋白结合的比例因子可以大大降低酸的绝对平均倍误差(AAFE)。使用 1C 模型,降低血浆蛋白结合的标度因子降低了酸和碱的微粒体清除率的 AAFE。使用渗透限制模型,只有酸需要此标度因子。酸的细胞溶质浓度增加的机制尚不清楚。我们还表明,对于肝细胞内在清除率的体外-体内相关性(IVIVC),1C 模型在机制上是合适的,因为肝细胞清除率应该代表从 ISF 到消除的净清除率。推导出了一个关系,该关系使用微粒体和肝细胞固有清除率来求解主动肝摄取清除率,但结果并不明确。最后,PTM 模型通常比 WSM 或 DM 模型表现更好,微粒体和肝细胞之间没有明显的优势。意义:从微粒体或肝细胞预测药物清除率仍然具有挑战性。各种肝模型(例如,完全混合、平行管和弥散)已被数学纳入肝脏以及全身生理基础药代动力学框架内。虽然由此产生的模型允许包括 pH 分配、通透性和主动摄取以预测药物清除率,但包括这些过程并没有改善微粒体和肝细胞的清除率预测。