增加贝叶斯基于网络的定义方法的可访问性,用于皮肤敏感性强度评估
Tomaz Mohoric1, Anke Wilm2, Stefan Onken2
1Edelweiss Connect GmbH, Hochbergerstrasse 60C, 4057 Basel, Switzerland.
Toxics
|September 27, 2024
概括
两种新的定义方法 (DAs) 通过使用免费可用的软件和新的方法 (如kDPRA) 来改进皮肤敏感性评估. 这些模型提高了化学安全评估的预测准确性和可靠性.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 皮肤病学 皮肤病学
背景情况:
- 皮肤敏感化是化学品的关键安全问题.
- 定义方法 (DAs) 整合了可靠的功效评估的各种方法.
- 新方法方法 (NAM) 提供了灵活性和更高的准确性.
研究的目的:
- 开发和验证两种新的DA模型,用于皮肤敏感性强度评估.
- 将kDPRA测试和开源QSAR模型集成到DA框架中.
- 使用自由可用的软件构建DA,以提高可访问性.
主要方法:
- 开发基于贝叶斯网络 (BN) 的两个DA模型.
- 纳入kDPRA (基于keratinoSens的活性测定) 数据用于体外评估.
- 包括来自开源量化结构-活动关系 (QSAR) 模型的in silico输入.
- 对四个局部淋巴结测试 (LLNA) 功率类进行验证.
主要成果:
- 新的DA模型实现了LLNA功率类的63%和68%的预测准确度.
- 性能与现有的BN DA模型相当或优于它.
- 贝叶斯网络信任指标有效地区分了可靠的 (高达87%的准确性) 和不可靠的预测.
结论:
- 开发的DA模型提供了一种可靠和可重复的方法来评估皮肤敏感性强度.
- 整合kDPRA和开源QSAR提高了DA的灵活性和准确性.
- 在化学安全评估中,BN信任度指标对于解释预测可靠性非常有价值.
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