Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance
Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance
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DOI：
10.1002/psp4.12321
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发表时间：
2018-09-01
影响因子：
3.5
通讯作者：
Isoherranen, Nina
中科院分区：
文献类型：
--
作者：
Huang, Weize;Isoherranen, Nina
肾清除率通常通过经验方法来预测，包括定量结构活性关系和异速生长缩放。最近，已经提出了使用计算机肾脏模型的机械预测方法。然而，经验缩放因子通常用于调整被动扩散或主动分泌，以可接受地预测肾脏清除率。本研究的目标是建立一种肾脏清除率模拟工具，允许根据体外渗透性数据预测肾脏清除率（滤过和 pH 依赖性被动重吸收）。根据人体生理学开发了 35 室生理学机械肾模型。该模型使用 46 种测试化合物进行了验证，包括中性物质、酸、碱和两性离子。使用对氨基马尿酸 (PAH)、西咪替丁、美金刚和水杨酸证明了将主动分泌和 pH 依赖性双向被动扩散纳入模型的可行性。开发的模型能够根据体外渗透性数据模拟肾脏清除率，预测的肾脏清除率在 87% 的测试药物观察到的两倍以内。
Renal clearance is usually predicted via empirical approaches including quantitative structure activity relationship and allometric scaling. Recently, mechanistic prediction approaches using in silico kidney models have been proposed. However, empirical scaling factors are typically used to adjust for either passive diffusion or active secretion, to acceptably predict renal clearances. The goal of this study was to establish a renal clearance simulation tool that allows prediction of renal clearance (filtration and pH-dependent passive reabsorption) from in vitro permeability data. A 35-compartment physiologically based mechanistic kidney model was developed based on human physiology. The model was verified using 46 test compounds, including neutrals, acids, bases, and zwitterions. The feasibility of incorporating active secretion and pH-dependent bidirectional passive diffusion into the model was demonstrated using para-aminohippuric acid (PAH), cimetidine, memantine, and salicylic acid. The developed model enables simulation of renal clearance from in vitro permeability data, with predicted renal clearance within twofold of observed for 87% of the test drugs.