Most Influential Physicochemical and In Vitro Assay Descriptors for Hepatotoxicity and Nephrotoxicity Prediction

Drug-induced organ injury is a major reason for drug candidate attrition in preclinical and clinical drug development. The liver, kidneys, and heart have been recognized as the most common organ systems affected in safety-related attrition or the subject of black box warnings and postmarket drug withdrawals. In silico physicochemical property calculations and in vitro assays have been utilized separately in the early stages of the drug discovery and development process to predict drug safety. In this study, we combined physicochemical properties and in vitro cytotoxicity assays including mitochondrial dysfunction to build organ-specific univariate and multivariable logistic regression models to achieve odds ratios for the prediction of clinical hepatotoxicity, nephrotoxicity, and cardiotoxicity using 215 marketed drugs. The multivariable hepatotoxic predictive model showed an odds ratio of 6.2 (95% confidence interval (CI) 1.7–22.8) or 7.5 (95% CI 3.2–17.8) for mitochondrial inhibition or drug plasma Cmax >1 μM for drugs associated with liver injury, respectively. The multivariable nephrotoxicity predictive model showed an odds ratio of 5.8 (95% CI 2.0–16.9), 6.4 (95% CI 1.1–39.3), or 15.9 (95% CI 2.8–89.0) for drug plasma Cmax >1 μM, mitochondrial inhibition, or hydrogen-bond-acceptor atoms >7 for drugs associated with kidney injury, respectively. Conversely, drugs with a total polar surface area ≥75 Å were 79% (odds ratio 0.21, 95% CI 0.061–0.74) less likely to be associated with kidney injury. Drugs belonging to the extended clearance classification system (ECCS) class 4, where renal secretion is the primary clearance mechanism (low permeability drugs that are bases/neutrals), were 4 (95% CI 1.8–9.5) times more likely to to be associated with kidney injury with this data set. Alternatively, ECCS class 2 drugs, where hepatic metabolism is the primary clearance (high permeability drugs that are bases/neutrals) were 77% less likely (odds ratio 0.23 95% CI 0.095–0.54) to to be associated with kidney injury. A cardiotoxicity model was poorly defined using any of these drug physicochemical attributes. Combining in silico physicochemical properties descriptors along with in vitro toxicity assays can be used to build predictive toxicity models to select small molecule therapeutics with less potential to cause liver and kidney organ toxicity.药物诱导的器官损伤是候选药物在临床前与临床药物研发阶段被淘汰的核心原因。肝脏、肾脏与心脏已被确认为安全相关研发淘汰事件中最常受累的器官系统，亦是出现黑框警告、上市后药物撤市的药物最常累及的器官系统。在药物发现与研发的早期阶段，研究人员常单独采用虚拟（in silico）理化性质计算与体外（in vitro）实验来预测药物安全性。本研究结合理化性质与包含线粒体功能异常检测在内的体外细胞毒性实验，构建了器官特异性单变量及多变量逻辑回归模型，基于215种上市药物，计算用于预测临床肝毒性、肾毒性与心脏毒性的优势比。多变量肝毒性预测模型显示，对于与肝损伤相关的药物，线粒体抑制或血浆Cmax＞1μM时，其优势比分别为6.2（95%置信区间（CI）1.7–22.8）与7.5（95% CI 3.2–17.8）。多变量肾毒性预测模型显示，对于与肾损伤相关的药物，血浆Cmax＞1μM、线粒体抑制或氢键受体原子数＞7时，其优势比分别为5.8（95% CI 2.0–16.9）、6.4（95% CI 1.1–39.3）与15.9（95% CI 2.8–89.0）。反之，总极性表面积≥75Å的药物发生肾损伤相关不良反应的概率降低79%（优势比0.21，95% CI 0.061–0.74）。属于延长清除分类系统（ECCS）4类的药物（其主要清除机制为肾脏分泌，即低渗透性酸碱/中性药物），发生肾损伤相关不良反应的概率为数据集内其他药物的4倍（95% CI 1.8–9.5）。与之相反，属于ECCS 2类的药物（其主要清除机制为肝脏代谢，即高渗透性酸碱/中性药物）发生肾损伤相关不良反应的概率降低77%（优势比0.23，95% CI 0.095–0.54）。基于上述药物理化性质特征构建的心脏毒性预测模型效果欠佳。综上，结合虚拟理化性质描述符与体外毒性实验，可用于构建毒性预测模型，以筛选出引发肝肾器官毒性潜力更低的小分子治疗药物。