A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria.,Journal of Medicinal Chemistry

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A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria.
Journal of Medicinal Chemistry ( IF 6.8 ) Pub Date : 2020-03-04 , DOI: 10.1021/acs.jmedchem.9b01995
Zhenhong Li ₁ , John Litchfield ₁ , David A Tess ₁ , Anthony A Carlo ₂ , Heather Eng ₂ , Christopher Keefer ₂ , Tristan S Maurer ₁

Affiliation

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule’s physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure–activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.

中文翻译：

基于生理的计算机模拟工具，用于评估与药物相关的结晶性尿症的风险。

肾脏肾单位中的药物沉淀可导致药物诱发的结晶性肾病（DICN）。为了帮助减轻早期药物发现中的这种风险，我们开发了一种基于生理的计算机模拟模型来预测大鼠，狗和人类的DICN。至少，DICN的可能性由对该分子的全身暴露水平，该分子的理化特性和肾脏的独特生理特性决定。因此，提出的模型考虑了这些性质，以便预测相对于沿肾单位溶解度的药物暴露。肾脏的关键生理参数已按照与先前报道一致的方式进行了整理。定量构效关系模型及体外测定用于估计模型的药物特异性理化输入。针对42种药物的尿排泄数据对提出的模型进行了校准，并且通过应用于20种其他药物证明了DICN预测的实用性。

更新日期：2020-03-04

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