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基于分子网络的致命性曲马多中毒中曲马多代谢物鉴定

Molecular Network-Based Identification of Tramadol Metabolites in a Fatal Tramadol Poisoning.

作者信息

Magny Romain, Auzeil Nicolas, Lefrère Bertrand, Mégarbane Bruno, Houzé Pascal, Labat Laurence

机构信息

Laboratoire de Toxicologie, Fédération de Toxicologie, AP-HP, Hôpital Lariboisière, 75006 Paris, France.

Université Paris Cité, CNRS, CiTCoM, 75006 Paris, France.

出版信息

Metabolites. 2022 Jul 19;12(7):665. doi: 10.3390/metabo12070665.

Abstract

Identification of xenobiotics and their phase I/II metabolites in poisoned patients remains challenging. Systematic approaches using bioinformatic tools are needed to detect all compounds as exhaustively as possible. Here, we aimed to assess an analytical workflow using liquid chromatography coupled to high-resolution mass spectrometry with data processing based on a molecular network to identify tramadol metabolites in urine and plasma in poisoned patients. The generated molecular network from liquid chromatography coupled to high-resolution tandem mass spectrometry data acquired in both positive and negative ion modes allowed for the identification of 25 tramadol metabolites in urine and plasma, including four methylated metabolites that have not been previously reported in humans or in vitro models. While positive ion mode is reliable for generating a network of tramadol metabolites displaying a dimethylamino radical in their structure, negative ion mode was useful to cluster phase II metabolites. In conclusion, the combined use of molecular networks in positive and negative ion modes is a suitable and robust tool to identify a broad range of metabolites in poisoned patients, as shown in a fatal tramadol-poisoned patient.

摘要

在中毒患者中鉴定外源性物质及其I/II期代谢产物仍然具有挑战性。需要使用生物信息学工具的系统方法来尽可能详尽地检测所有化合物。在此,我们旨在评估一种分析流程,该流程使用液相色谱与高分辨率质谱联用,并基于分子网络进行数据处理,以鉴定中毒患者尿液和血浆中的曲马多代谢产物。通过在正离子和负离子模式下采集的液相色谱与高分辨率串联质谱数据生成的分子网络,能够鉴定出尿液和血浆中的25种曲马多代谢产物,其中包括四种甲基化代谢产物,这些产物此前在人体或体外模型中均未被报道过。虽然正离子模式对于生成结构中显示二甲基氨基基团的曲马多代谢产物网络是可靠的,但负离子模式对于聚类II期代谢产物很有用。总之,如在一例致命的曲马多中毒患者中所示,正离子和负离子模式下分子网络的联合使用是一种适用于鉴定中毒患者中多种代谢产物的强大工具。

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