证据权重:标准和应用
Erika Colombo1, Giuseppa Raitano1, Edoardo Luca Viganò1
1Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Methods in molecular biology (Clifton, N.J.)
|September 23, 2024
概括
非试验方法 (NTMs) 通过整合来自不同来源的数据来加强化学风险评估. 一个新的指导方针和SWAN工具有助于结合in silico模型和阅读横向以获得更可靠的毒理学评估.
科学领域:
- 毒理学和化学风险评估.
- 计算毒理学和in silico建模
- 监管科学是一种监管科学.
背景情况:
- 传统的毒理学评估依赖于既有机制.
- 非测试方法 (NTM) 为化学风险评估提供了有价值的替代方案.
- 整合来自多个NTM的数据增加了对评估结果的信心.
研究的目的:
- 讨论从NTM集成的数据,特别是在模型和交叉阅读.
- 提交欧洲食品安全局 (EFSA) 关于证据整合和权衡的指导方针.
- 用实例和SWAN工具来展示这些整合原则的应用.
主要方法:
- 审查和讨论EFSA关于证据整合的指导方针.
- 手动和自动 (SWAN工具) 集成来自in silico模型的数据.
- 综合NTM数据应用于化学风险评估.
主要成果:
- 不同的NTM的整合,如in silico模型和交叉阅读,由于结果相互矛盾,可能是具有挑战性的.
- 食品安全局的指导方针为整合和权衡来自不同来源的证据提供了一个框架.
- SWAN工具为手动或自动化数据集成提供了一个实用的方法.
- 实践示例证明了风险评估的集成in silico方法的有效性.
结论:
- 整合来自多个NTM的数据,包括in silico模型和交叉阅读,可以提高化学风险评估的可靠性.
- 欧洲食品安全局指南和SWAN工具为毒理学家和监管机构提供了宝贵的资源.
- 这些方法提高了NTM对危险化学品评估的适用性和有效性.
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