Abstract：
Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.
摘要：
基于不良结果途径（AOP）框架开发机械非动物测试方法必须纳入与目标毒性相关的分子和细胞关键事件。使用来自体外测定和化学结构的数据，我们的目的是建立一个混合模型来预测肝毒性。我们首先策划了用于肝毒性建模的869种化合物的参考数据集。然后，我们将它们与PubChem的现有体外毒性数据进行了比较。在2560个试验中，我们选择了线粒体膜电位（MMP）测定，一种高通量筛选（HTS）工具，可以测试化学破坏物的线粒体功能。应用机器学习来开发定量结构-活性关系(QSAR)模型,在MMP测定中测试2536种化合物以筛选新化合物。MMP检测结果,包括QSAR模型输出，获得了正确分类比(CCR)为0.59的参考组化合物的肝毒性预测。通过包括37个结构性警报（CCR=0.8），预测性得到了改善。我们通过在人HepG2肝癌细胞中测试37个参考集化合物来验证我们的模型，并可靠地预测它们的肝毒性（CCR=0.79）。本研究引入了一种新颖的AOP建模策略，该策略结合了公共HTS数据，计算建模，和实验测试来预测化学肝毒性。
