使用体外测定数据和化学结构预测化学诱导的急性毒性
Xi Luo1, Tuan Xu1, Deborah K Ngan1
1Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA.
Toxicology and applied pharmacology
|September 9, 2024
概括
来自Tox21联盟的体外测定数据有效预测化学急性毒性,为动物试验提供了替代方案. 使用这些数据的机器学习模型显示出强大的预测能力,用于识别有毒化合物.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 环境中的化学物质和药物开发过程中存在急性毒性的风险.
- 在体外测试为传统的体外动物毒性测试提供了替代方案.
- 美国Tox21联盟已经开发了一个大约1万种化合物的库,用于高通量查.
研究的目的:
- 评估Tox21的预测能力,以检测急性系统性毒性的体外测定数据.
- 为了比较化学结构信息与测试数据在毒性预测中的性能.
- 确定关键的Tox21测定和与急性毒性相关的化学特征.
主要方法:
- 开发了使用四种机器学习算法的预测模型:随机森林,天真贝叶斯,极端梯度增强和支持向量机.
- 使用接收器操作特征曲线 (AUC-ROC) 下面的面积来评估模型性能.
- 应用验证的模型来预测Tox21 10K化合物库的急性毒性.
主要成果:
- 基于化学结构的模型和Tox21测定数据都显示出急性毒性的显著预测能力 (AUC-ROC:分别为0.830.93和0.730.79).
- 预计Tox21 10K库中的大多数化合物是无毒的.
- 乙胆酶 (AChE) 抑制和p53诱导试验被认为是预测急性毒性的高度信息.
结论:
- 来自Tox21联盟的体外测定数据对于预测化学急性毒性非常有价值.
- 集成测试数据的机器学习模型为动物试验提供了强大的替代方案.
- 特定的测定和化学品类 (例如,有机酸盐,碳酸盐) 是急性毒性的关键指标.
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