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集成GC-EI-HRMS和LC-ESI-HRMS工作流程,以及分子网络,用于未针对性地识别新的精神活性物质

Romain Magny¹, Mathieu Le Seigle², Thomas Schiestel²

¹Laboratoire de Toxicologie, Fédération de Toxicologie, AH-HP, Hôpital Lariboisière, 75010, Paris, France; Université Paris-Cité, INSERM, Optimisation Thérapeutique en Neuropharmacologie OTEN U-1144, 75006, Paris, France.

|February 21, 2026

概括

这项研究将GC-EI-HRMS和LC-ESI-HRMS与用于非向毒理学查 (UTS) 的分子网络集成. 这种双平台方法增强了异生菌识别和代谢物注释,提高了复杂样品的准确性.

科学领域:

分析化学分析化学
毒理学毒理学毒理学
代谢学代谢学代谢学

背景情况:

非定向毒理学查 (UTS) 面临挑战,因为异生生物多样性和分析平台的局限性.
液体染色学高分辨率质谱 (LC-HRMS) 缺乏对异生菌的全面光谱数据库,包括新的精神活性物质 (NPS).
气色谱-质谱 (GC-MS) 具有广泛的光谱库,但由于单位质量解卷,复杂矩阵的灵敏度有限.

研究的目的:

实施和评估一个整合性工作流程,将GC-EI-HRMS和LC-ESI-HRMS与分子网络 (MN) 结合起来,以改进外来生物的识别.
为了利用高分辨率的质量测量来提高解卷选择性和精确的质量EI光谱匹配.
使用LC-ESI-HRMS进行前体离子和MS/MS光谱分析,使结构确认和代谢物注释成为可能.

主要方法:

集成GC-EI-HRMS和LC-ESI-HRMS的工作流.
分子网络 (MN) 的应用,用于数据组织和分析.
利用高分辨率质谱仪进行选择性解卷和光谱匹配.
分析涉及环胺和丁的中毒病例.

主要成果:

高分辨率的质量测量提高了解卷选择性,并允许直接匹配到光谱库.
整合性工作流确认了拟议的结构,并使第一阶段和第二阶段的代谢物注释成为可能.
从GC-HRMS数据构建的分子网络提炼了光谱匹配,改善了注释一致性,并在中毒病例中确定了新的代谢物.

结论:

双平台策略克服了仅用于LC-HRMS的外来生物识别工作流程的局限性.
集成基于GC和LC的分子网络可以提高对外来生物识别的信心.
这种方法为广泛的暴露应用和毒理学查提供了新的视角.

关键词:

在GC-HRMS中使用.分子网络是分子网络.对毒理学查进行毒理学查.异种生物的异种生物.

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