在重复暴露下慢性毒性的定量不良结果路径建模的概念验证
Shigeaki Ito1, Sayak Mukherjee2, Kazuo Erami3
1Scientific Product Assessment Center, Japan Tobacco Inc., 6-2, Umegaoka, Aoba-ku, Yokohama, Kanagawa, 227-8512, Japan. shigeaki.ito@jt.com.
本研究引入了一个动态的贝叶斯网络模型,用于在重复化学暴露下量化不良结果途径 (AOPs). 该模型有助于识别早期的毒性指标,并揭示了AOP随着时间的推移的动态性质.
科学领域:
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
- 风险评估 风险评估
背景情况:
- 不良结果途径 (AOPs) 对于理解化学作用模式 (MOE) 是至关重要的.
- 量化AOP通常涉及单次高剂量暴露的数据,限制它们在复杂场景中的应用.
- 将时间动态纳入AOP建模仍然是风险评估的一个重大挑战.
研究的目的:
- 开发和验证一个定量性的不良结果路径 (AOP) 框架,包括重复暴露.
- 使用贝叶斯网络 (BN) 分析来建模毒性的时间动态.
- 探索AOP修剪技术,以识别动态因果结构.
主要方法:
- 使用虚拟数据集量化的假设AOP的开发.
- 应用静态和动态贝叶斯网络 (DBN) 模型来分析重复暴露数据.
- 在数据驱动的 AOP 修剪中实现基于 lasso 的子集选择.
主要成果:
- DBN模型成功地根据上游关键事件 (KEs) 计算了不利结果的概率.
- 通过概率分析确定了潜在的不良结果的早期指标.
- 证明了AOP的因果结构可以是动态的,并随着时间的推移而变化.
结论:
- DBN框架提供了一个可行的方法来量化在重复化学侮辱下AOPs.
- 这种方法提高了早期毒性生物标志物的识别.
- 这项研究强调了对重复暴露的生物反应的动态性质,扩大了AOP的适用性.
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