Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 467-8603, Japan.
Division of Pathology, National Institute of Health Sciences, Kawasaki 1210-9501, Japan.
Toxicol Sci. 2018 Apr 1;162(2):667-675. doi: 10.1093/toxsci/kfx287.
In silico prediction for toxicity of chemicals is required to reduce cost, time, and animal testing. However, predicting hepatocellular hypertrophy, which often affects the derivation of the No-Observed-Adverse-Effect Level in repeated dose toxicity studies, is difficult because pathological findings are diverse, mechanisms are largely unknown, and a wide variety of chemical structures exists. Therefore, a method for predicting the hepatocellular hypertrophy of diverse chemicals without complete understanding of their mechanisms is necessary. In this study, we developed predictive classification models of hepatocellular hypertrophy using machine learning-specifically, deep learning, random forest, and support vector machine. We extracted hepatocellular hypertrophy data on rats from 2 toxicological databases, our original database developed from risk assessment reports such as pesticides, and the Hazard Evaluation Support System Integrated Platform. Then, we constructed prediction models based on molecular descriptors and evaluated their performance using independent test chemicals datasets, which differed from the training chemicals datasets. Further, we defined the applicability domain (AD), which generally limits the application for chemicals, as structurally similar to the training chemicals dataset. The best model was found to be the support vector machine model using the Hazard Evaluation Support System Integrated Platform dataset, which was trained with 251 chemicals and predicted 214 test chemicals inside the applicability domain. It afforded a prediction accuracy of 0.76, sensitivity of 0.90, and area under the curve of 0.81. These in silico predictive classification models could be reliable tools for hepatocellular hypertrophy assessments and can facilitate the development of in silico models for toxicity prediction.
为了降低成本、时间和动物测试，需要对化学品毒性进行计算机预测。然而，预测肝细胞肥大，这通常会影响重复剂量毒性研究中无观察到不良作用水平的推导，是困难的，因为病理发现多种多样，机制在很大程度上是未知的，而且存在各种各样的化学结构。因此，有必要开发一种无需完全了解其机制即可预测多种化学品肝细胞肥大的方法。在这项研究中，我们使用机器学习，特别是深度学习、随机森林和支持向量机，开发了肝细胞肥大的预测分类模型。我们从 2 个毒理学数据库中提取了大鼠肝细胞肥大数据，我们的原始数据库是从农药等风险评估报告中开发的，以及危险评估支持系统综合平台。然后，我们基于分子描述符构建了预测模型，并使用来自训练化学物质数据集的独立测试化学物质数据集评估了它们的性能。此外，我们定义了适用性域（AD），一般将其限制在与训练化学物质数据集结构相似的化学物质的应用范围内。使用危险评估支持系统综合平台数据集训练的支持向量机模型被发现是最好的模型，该模型使用了 251 种化学物质，并预测了适用性域内的 214 种测试化学物质。它的预测准确率为 0.76，灵敏度为 0.90，曲线下面积为 0.81。这些计算机预测分类模型可以成为肝细胞肥大评估的可靠工具，并有助于毒性预测的计算机模型的开发。
