开发和应用一个全面的非向查策略,用于阿里斯托洛希酸类似物
Xiaoqi Chen1,2,3, Wenlin Wu2,3,4,5, Hongbing Sun1,2,3,6
1Chengdu Institute of Organic Chemistry, Chinese Academy of Sciences, Chengdu 610041, China.
Analytical chemistry
|January 24, 2024
概括
一个新的Python工具包,AAAs_finder,允许对阿里斯托洛基酸类型 (AAA) 进行全面的非目标选. 这一策略克服了在各种样本中识别这些有毒化合物的挑战.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 毒理学 毒理学 毒理学
背景情况:
- 阿里斯托洛希克酸类似物 (AAA) 是天然存在的,致癌的和有毒的化合物. 它们对制药安全和环境健康构成重大风险.
- 由于它们的结构多样性和缺乏特征性的质谱碎片化模式,选AAA具有挑战性.
研究的目的:
- 开发一个全面的非目标选策略和一个基于Python3的工具包 (AAAs_finder) 来识别阿里斯托洛基酸类型 (AAA).
- 克服传统选方法的局限性,这些选方法与AAA的结构多样性和光谱特征作斗争.
主要方法:
- 开发一个基于Python3的工具包,AAAs_finder,实施一种新的选策略.
- 创建虚拟结构和UV-Vis光谱数据库.
- 使用精确的质量,紫外线光谱,双重质谱 (MS/MS) 解释和保留时间 (RT) 预测来对候选结构进行排名.
主要成果:
- 该AAAs_finder工具包成功地在假设样本中识别了所有目标阿里斯托洛基酸类型,超过了其他两个先进工具.
- 该战略展示了可行性,低虚假阳性/负率,以及在不同工具中良好的可重复性和灵敏性.
- 在现实世界样本中成功应用选AAA的策略,包括草药,土和水.
结论:
- 拟议的非向查策略和AAAs_finder工具集有效地解决了识别阿里斯托洛基酸类型的挑战,无论特征质谱碎片化如何.
- 这种方法对于选结构多样化的化合物是有价值的,并且具有广泛的适用性,超出了阿里斯托洛基酸类似物.
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