评估通过克莱默分类体系的 in silico 和专家应用得出的判断的一致性
James W Firman1, Alan Boobis2, Heli M Hollnagel3
1Liverpool John Moores University, Liverpool, United Kingdom.
概括
克莱默分类方案显示高专家一致,但与计算工具相对适度一致. 预测毒理学的差异来自解释,软件和规则修订,影响化学危险评估.
科学领域:
- 预测性毒理学 预测性毒理学
- 计算化学的计算化学
- 化学安全评估 化学安全评估
背景情况:
- 克莱默分类方案广泛用于毒理学评估中的化学品分类.
- 克莱默计划的危险归因存在不一致性,特别是在食品相关物质方面.
- 对于应用或调整分类规则的用户来说,了解这些差异至关重要.
研究的目的:
- 调查克莱默分类系统中不一致的来源.
- 评估专家判断和该计划的in silico实施之间的一致性.
- 提高用户对应用克莱默分类规则的潜在问题的认识.
主要方法:
- 汇集了3000多个化合物的数据集,来自多个专家组的克莱默类分配.
- 使用了Toxtree和OECD QSAR工具箱中的Cramer方案输出,包括修订的Cramer决策树.
- 评估了专家判断之间的一致性,以及专家和in silico呼叫之间的一致性.
主要成果:
- 对于克莱默分类的专家间的共识非常高 (≥97%).
- 专家和in silico Cramer调用之间的一致性更为温和 (∼70%).
- 确定了22种化学物质的分类,作为解释,软件或规则修订引起的重大分歧来源.
结论:
- 虽然专家们在很大程度上同意克莱默分类,但计算工具显示适度一致.
- 预测毒理学评估中的差异与主观解释,软件异常和修订规则有关.
- 了解这些因素对于准确应用和调整克莱默分类体系至关重要.
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