(Q) SAR方法用于预测药物化合物中化程度
Krystle Reiss1, Roustem Saiakhov1, Suman Chakravarti1
1MultiCASE Inc., 5885 Landerbrook Dr. #210, Mayfield Heights, Ohio 44124, United States.
Chemical research in toxicology
|February 27, 2025
概括
研究人员开发了两种模型来预测药品中的突变性尼特罗斯胺形成. 这些模型,一个是统计模型,一个是基于规则的模型,达到~80%的准确性,有助于防止这些有害杂质.
科学领域:
- 制药化学 制药化学 的相关文章
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 自2018年以来,致变性尼托胺已被确定为制药中的杂质.
- 预测和预防尼托胺的形成是制药安全研究的关键领域.
- 化试验程序是评估化易感性的关键实验方法.
研究的目的:
- 开发尼托胺形成的预测模型.
- 根据实验数据构建结构-活动关系 (SAR) 模型.
- 为防止药品中转基因尼托拉胺杂质的预防提供工具.
主要方法:
- 从科学文献中编制的实验性化数据.
- 在207个含分子上使用图形神经网络开发了一个统计模型.
- 创建了一个基于专家规则的模型,其中包含来自化文献的15条规则.
- 这两种模型都预测了中心或胺产物形成的化可能性.
主要成果:
- 统计模型和基于规则的模型都实现了大约80%的准确性.
- 根据现有数据进行训练的统计模型显示,对将氨基酸分类为非化氨基的偏见.
- 基于规则的模型倾向于支持预测表明化.
- 这些模型提供互补的预测,联合使用可能会提高可靠性.
结论:
- 两个新的SAR模型有效地预测了尼托胺形成的易感性.
- 基于统计和规则的模型为分子化提供了有价值的见解.
- 结合这些模型可以减轻个体偏见,提高尼托拉胺风险评估的准确性.
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