基于受体结合/生物活化/机理解释的化学物质的识别,与潜在的引起肝毒性相关,并支持基于结构活性关系的交叉阅读
Shengde Wu1, George Daston1, Jane Rose1
1Central Product Safety Department, The Procter & Gamble Company, 8700 Mason Montgomery Rd, Cincinnati, OH 45040 USA.
这项研究开发了一个决策树,通过分析化学结构及其机制来识别潜在的肝毒性物质. 该工具有助于预测肝毒性,并加强化学安全评估的结构活性关系 (SAR) 分析.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 肝脏是毒理学的主要标器官,需要强大的方法来识别肝毒素.
- 化学结构警报对于*in silico* (计算) 毒性预测和结构-活性关系 (SAR) 分析至关重要.
研究的目的:
- 开发基于SAR的专家系统决策树,用于选肝毒性化学物质.
- 根据受体结合或生物激活途径对化学品进行聚类,以改进毒理学评估.
主要方法:
- 对大约1180种化学品的肝毒性信息进行了审查.
- 开发了一个决策树,根据受体结合,反应性代谢物 (RMs) 和作用方式 (MOA) 将化学物质分为16个主要类别和102个子类别.
- 化学受体结合,新陈代谢和机械学数据的利用知识.
主要成果:
- 创建了一个决定树,将化学物质分为16个类别 (4个受体结合剂,11个RM关联,1个各种) 和102个子类别.
- 每个子类别都与特定的MOA或结构特征相关.
- 该系统基于对受体结合和/或RMS的结构性警报来识别潜在的肝毒性物质.
结论:
- 开发的决策树有效地选潜在的肝毒性物质.
- 该工具支持基于SAR的跨读评估,并有助于填补化学安全评估中的数据缺口.
- 它是毒理风险评估的证据权重决定中的一个有价值的组成部分.
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