对不良结果途径的权重网络分析解码了环境毒性多层次机制
Huajie Yang1,2,3, Kaiyi Zhang4, Yue Wang5
1Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, 300070, PR China.
Environmental science and ecotechnology
|February 9, 2026
概括
一个新的计算平台,AOP-ExpoVis,将化学暴露数据与生物途径集成,以预测化学毒性. 该工具有助于优先考虑环境健康风险,了解化学物质引起的疾病.
科学领域:
- 环境健康 环境健康
- 计算毒理学计算毒理学
- 系统生物学 系统生物学
背景情况:
- 传统的毒性测试很难跟上大量合成化学品的步伐.
- 现有的框架,如不良结果途径 (AOP) 和暴露组范式,在整合多尺度数据以预测疾病方面存在局限性.
- 对于将化学物质暴露,生物机制和疾病结果结合在一起的方法,存在着至关重要的需求.
研究的目的:
- 开发和验证一个集成的计算平台,AOP-ExpoVis,以优先考虑化学诱导疾病机制.
- 将暴露组数据与AOP知识进行协同,以生成可测试的机械学假设.
- 在预测毒理学中克服传统单路径方法的局限性.
主要方法:
- 开发了AOP-ExpoVis,这是一个整合化学,基因,表型和疾病关联的平台.
- 利用加权的表型-疾病评分算法来优先考虑致病机制.
- 绘制了确定关键的表型,为机械推理策划了途径.
- 使用BDE-47,和PFAS的案例研究验证了平台.
主要成果:
- AOP-ExpoVis成功地确定了保存和化学特异性毒性通路,包括烯碳化合物受体激活和脂质代谢中断.
- 该平台在多种化学物质暴露中优先考虑致病机制方面表现出准确性.
- 案例研究证实了该平台能够产生可测试的机械学假设的能力.
结论:
- 在毒理学中,AOP-ExpoVis提供了一个开源工具,用于快速的机械推理.
- 该平台通过使化学危害的系统优先级能够推进预测毒理学.
- 这项工作支持在数据丰富的环境中完善监管风险评估.
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