Fera Science Limited, Sand Hutton, York, YO41 1LZ, UK.
Fera Science Limited, Sand Hutton, York, YO41 1LZ, UK.
Food Chem Toxicol. 2020 Aug;142:111494. doi: 10.1016/j.fct.2020.111494. Epub 2020 Jun 14.
There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has the potential to provide a rapid and cost-effective method to identify compounds which may trigger steatosis. Although models exist to predict key molecular initiating events of steatosis such as nuclear receptor binding, we are aware of no models to predict the apical effect steatosis. In this study, we describe the development of a QSAR model to predict steatosis using freely available machine learning tools. It was built using a dataset of 207 pharmaceuticals and pesticides which were identified as steatotic or non-steatotic from existing data from in vivo human and animal studies. The best performing model developed using the linear discriminant analysis module in TANAGRA, based on four chemical descriptors, had an accuracy of 70%, a sensitivity of 66% and a specificity of 74%. The expansion of the steatosis dataset to other chemical types, to enable the development of further models, would be of benefit in the identification of compounds with a range of mechanisms of action contributing to steatosis.
有多种类型的肝脂肪变性，其中非酒精性脂肪性肝病可能是由接触化学物质和环境污染物引起的，最为普遍，是潜在的主要健康风险。定量构效关系（QSAR）建模有可能提供一种快速且具有成本效益的方法来识别可能引发脂肪变性的化合物。虽然存在预测脂肪变性关键分子起始事件的模型，如核受体结合，但我们不知道预测脂肪变性顶端效应的模型。在这项研究中，我们描述了使用免费的机器学习工具开发预测脂肪变性的 QSAR 模型。它是使用从体内人类和动物研究的现有数据中确定为脂肪变性或非脂肪变性的 207 种药物和农药的数据集构建的。使用 TANAGRA 中的线性判别分析模块开发的性能最佳的模型基于四个化学描述符，准确率为 70%，灵敏度为 66%，特异性为 74%。将脂肪变性数据集扩展到其他化学类型，以开发进一步的模型，将有助于识别具有多种作用机制导致脂肪变性的化合物。
