绘制未知的地图:基于的分子网络工作流程,用于全面识别新的精神活性物质和类似物质
Carla Fournié1,2, Laurence Labat3,4, Mathieu Le Seigle3
1Laboratoire de Toxicologie Biologique, Fédération de Toxicologie, Hôpital Lariboisière, Assistance Publique - Hôpitaux de Paris, 2, Rue Ambroise Paré, 75010, Paris, France. carla.fournie@etu.u-paris.fr.
Analytical and bioanalytical chemistry
|December 11, 2025
概括
一种新的分析方法使用液态色谱和质谱来检测生物样本中未知的新型精神活性物质 (NPS). 这种数据库独立的工作流可以加强新兴药物的毒理学查.
科学领域:
- 法医毒理学 法医毒理学
- 分析化学 分析化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 新型精神活性物质 (NPS) 由于其快速出现和结构多样性,对传统的毒理学查构成重大挑战.
- 现有的方法通常依赖于对已知化合物的有针对性的检测,限制了它们识别新物质或未知的物质的能力.
研究的目的:
- 开发和验证一种新的,独立于数据库的分析工作流程,用于在复杂的生物矩阵中识别未知的或新兴的NPS.
- 建立一种综合方法,结合先进的色谱学和高分辨率质谱学,用于检测新的精神活性结构及其代谢物.
主要方法:
- 开发了一种工作流程,整合了样品制备,直角液体色谱 (逆相和水友相互作用) 和高分辨率并列质谱 (LC-HRMS/MS).
- 评估了三种提取协议,其中协议P1 (蛋白沉和分析物度) 显示出最佳性能.
- 基于的分子网络 (ABMN) 使用122种化合物 (Mix122) 的混合物构建,并与临床样本集成以实现特征连接.
主要成果:
- 协议P1显示了高的分析物回收率 (90%) 和出色的染色质量,达到最低的识别极限.
- 该方法使MS/MS能够在50ng/mL时获得100%的化合物,在5ng/mL时获得97%的化合物,在0.5ng/mL时获得42%的化合物.
- 正对角色谱 (RP-LC和HILIC) 改善了分析物覆盖范围,ABMN成功整合了临床样本,识别了类似的类似物,如布罗马佐拉姆和甲胺.
结论:
- 开发的基于基分子网络的LC-HRMS/MS策略提供了一个强大的和可转移的分析平台.
- 这种方法提供了对未知NPS的全面和敏感选能力,即使在微量水平下降到1 ng/mL.
- 该工作流有效地解决了针对新兴和结构多样化的新精神活性物质的有针对性的查的局限性.
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