通过整合ToxCastTM数据,机器学习和实验验证,优先考虑类化学物质
Xiaoliu Shi1, Lingbing Jin1, Xiaochun Ma1
1School of Life Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China.
Environment international
|January 23, 2026
概括
识别强烈的稳定性化学物质对于代谢疾病调节至关重要. 这项研究整合了高通量查数据和预测建模,以优先考虑导致脂肪肝疾病的化学物质,并通过体内和体外实验验证实了这些发现.
科学领域:
- 毒理学 毒理学 毒理学
- 环境健康 环境健康
- 代谢性疾病研究研究
背景情况:
- 肝脂肪症 (脂肪肝疾病) 是与环境暴露相关的日益严重的全球健康问题.
- 有效的以风险为导向的监管需要识别和优先考虑具有高稳性功能的化学品.
研究的目的:
- 开发和验证用于识别高强度稳性化学物质的预测模型.
- 将高通量查数据与体内和体外实验验证进行整合.
主要方法:
- 利用了肝硬化不良结果路径框架来实现肝硬化.
- 来自ToxCastTM数据库的综合ToxPi得分与斑马鱼体内验证.
- 雇员支持向量机 (SVM) 和随机步行与重启算法用于预测和分类.
主要成果:
- 开发了一个SVM模型,在培训套件上准确率为91.7%,在外部验证上准确率为77.1%.
- 分类345种精选化学物质:37.97%具有高强度,18.84%具有中等强度,43.19%无效.
- 对于新出现的污染物,如异二酸和四博二甲 bis ((2-hydroxyethyl) 乙醇等,已证实具有很高的稳性作用.
结论:
- 综合方法有效地优先考虑高强度的稳性化学物质.
- 这些发现凸显了几个新出现的环境污染物的风险.
- 这种方法支持可靠的毒理和环境风险评估.
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