从公共度响应数据对复杂毒性终点的层次机制建模
Elena Chung1,2, Daniel P Russo2, Lauren M Aleksunes3
1Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States.
一个新的层次模型组织了高通量查 (HTS) 数据来预测化学毒性. 这一框架将化学生物活性与不良结果联系在一起,有助于风险评估和药物发现.
科学领域:
- 计算毒理学计算毒理学
- 化学安全评估 化学安全评估
- 不良结果路径 (AOP) 研究
背景情况:
- 高通量查 (HTS) 产生了大量的化学毒性数据.
- 解释HTS数据用于预测建模具有挑战性,因为数据不一致和各种实验设计.
研究的目的:
- 为构建和解释HTS度-响应数据开发一个层次化的机制建模框架.
- 创建基于AOP的模型,将化学生物活性与不良结果联系起来.
主要方法:
- 整合了来自455个PubChem测试的精选数据,映射到216个蛋白质目标和103个WikiPathways.
- 在生物层次层次结构中组织测试数据,以构建基于AOP的模型.
- 通过整合蛋白质活性和途径扰乱,生成途径级毒性得分.
主要成果:
- 开发了一个框架,将化学生物活性与5种体内毒性终点 (急性全身性,母体性,发育性,肝毒性) 联系起来.
- 103个途径在统计学上与这些毒性终点相关.
- 这些模型量化了化合物的效力,并预测了各种毒性结果.
结论:
- 层次框架增强了从HTS数据中路径级效应的机械解释性.
- 为化合物排名,功效评估和危害预测提供了定量基础.
- 支持计算毒理学,化学风险评估和早期药物发现.
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