Abstract：
Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health. However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs\' hepatotoxicity using network analysis coupled with machine learning models. Considering the structural and functional similarities shared by BPs with Bisphenol A (BPA), we first integrated hepatic disease related genes from multiple databases into BPA-Gene-Phenotype-hepatic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatotoxicity of 20 emerging BPs and provided new insights into how BPs\' structure features contributed to biologic functions with limited experimental data. Additionally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating event. Overall, our study provides a computational approach to predict the hepatotoxicity of emerging BPs.
摘要：
毒理学研究已经证明了几种双酚类似物（BP）的肝毒性，一种普遍存在的内分泌干扰物。不良结果路径（AOP）的发展极大地促进了人类健康的快速风险评估。然而,新出现的BPs缺乏体外和体内数据限制了对这些合成化学品的危害评估.这里,我们的目标是开发一种新的策略，使用网络分析和机器学习模型快速预测BPs的肝毒性。考虑到BP与双酚A（BPA）的结构和功能相似性，我们首先将来自多个数据库的肝病相关基因整合到BPA-基因-表型-肝毒性网络中,并对其进行计算AOP(cAOP).通过cAOP网络和传统的机器学习方法，我们对20个新出现的BPs的肝毒性进行了评分,并在有限的实验数据下提供了新的认识,以了解BPs的结构特征如何促进生物学功能。此外,我们使用分子对接评估了新出现的BPs和ESR1之间的相互作用,并提出了一个AOP框架,其中ESR1是一个分子启动事件.总的来说,我们的研究提供了一种计算方法来预测新出现的BPs的肝毒性。
