使用贝叶斯网络模型推导皮肤敏感化风险评估的连续起点
Fleur Tourneix1, Leopold Carron1, Lionel Jouffe2
1L'Oréal, Research & Innovation, 1Eugène Schueller, 93600 Aulnay-sous-Bois, France.
化品行业领先于无动物风险评估,使用定义的方法来预测皮肤敏感性强度. 本研究引入了贝叶斯网络模型来推导连续的起点,提高下一代风险评估的可靠性.
科学领域:
- 化品科学 化品科学
- 毒理学 毒理学 毒理学
- 计算化学计算化学
背景情况:
- 化品成分法规在采用新方法方法 (NAM) 方面取得了进展.
- 化品行业是无动物下一代风险评估 (NGRA) 的先驱,使用定义方法 (DA).
研究的目的:
- 开发和验证一种无动物DA,用于预测皮肤敏化功效.
- 使用NGRA建立一个持续的出发点 (PoD) 来进行风险评估.
主要方法:
- 贝叶斯网络DA (SkinSens-BN) 是使用297种物质的局部淋巴结测试数据开发的.
- 计算了SkinSens-BN概率的加权和来得出连续的PoDs.
- 对预测分配了信心级,以告知不确定性评估.
主要成果:
- 在SkinSens-BN实现预测性能与其他DA可比.
- 对于非敏感剂的衍生POD是不同的,77%比LLNAEC3值更保守.
- 这种方法表明,它有望为NGRA提供信息.
结论:
- 开发的PoD衍生方法有助于可靠的皮肤敏感化NGRAs.
- 这种方法支持化品行业在推进无动物试验方面的作用.
- 定义的方法和NGRA对于现代风险评估至关重要.
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