混合非动物建模:一种机械方法来预测化学肝毒性
Elena Chung1, Xia Wen2, Xuelian Jia1
1Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA.
Journal of hazardous materials
|April 27, 2024
概括
这项研究开发了一个混合模型,使用体外测试和计算方法来预测化学肝毒性. 该方法结合了高通量查数据和结构分析,以提高识别肝毒素的准确性.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 开发非动物试验方法对于评估化学毒性至关重要.
- 不良结果途径 (AOP) 框架需要整合分子和细胞事件.
- 预测肝毒性需要了解肝损伤的机制.
研究的目的:
- 创建一个混合模型来预测肝毒剂.
- 将体外测定数据与化学结构相结合,用于毒性预测.
- 在AOP框架内推进机械非动物试验策略.
主要方法:
- 策划了869种化合物的参考数据集,用于肝毒性建模.
- 利用PubChem进行体外毒性数据,选择线粒体膜潜力 (MMP) 试验.
- 在2536个化合物上使用机器学习开发了定量结构-活性关系 (QSAR) 模型.
- 整合了37个结构警报,以提高模型的预测性.
- 使用人类HepG2肝瘤细胞验证了模型.
主要成果:
- 最初的MMP测定和QSAR模型预测了肝毒性,正确分类比 (CCR) 为0.59.
- 整合结构性警报使预测的CCR提高到0.8.8.
- 在HepG2细胞中的实验验证实现了0.79.7的可靠预测CCR.
- 证明了结合HTS数据,计算建模和实验测试的有效性.
结论:
- 引入了一种基于AOP的新型建模策略,用于预测化学肝毒性.
- 混合方法有效地结合了公共HTS数据,计算建模和实验验证.
- 这一策略提供了一种有前途的非动物测试方法,用于识别肝脏毒性物质.
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