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• W4283165164 abstract "Murraya koenigii (M. koenigii) is recognized as one of the most significant Indian medicinal plants, due to its therapeutic properties. In fact, various biological activities, including anti-inflammatory and antifungal, are attributed to carbazole alkaloids (CAs) and their metabolites, but it is still difficult to achieve a full classification of these compounds because of their heterogeneous structures. For this reason, the development of new strategies for their identification is necessary. In this work, a reliable method, including the simultaneous quantification of three main CAs of Murraya (Mahanimbine, Koenimbine and Koenigicine) and a putative identification for other compounds belonging to the huge family of CAs, was developed by means of HPLC-MS/MS. Also, an efficient extraction procedure followed by a suitable clean-up step was presented, in order to obtain reliable recoveries (resulted from 60 to 85% for all the analytes). The analyses were performed by using predictive multi experiment approach based on information-dependent acquisition (IDA), coupling multiple reaction monitoring (MRM) and precursor ion (PI) as survey scans, and enhanced product ion scan (EPI) as dependent scan. Competitive Fragmentation Modeling-ID (CFM-ID) was used to predict MS/MS spectra from the chemical structures of the compounds in order to create a suitable MRM inclusion list. The obtained results showed that this method can simultaneously provide quantitative information for the target analytes (Mahanimbine (7.22–5.62 mg/kg), Koenimbine (1.26–1.62 mg/kg) and Koenigicine (0.44–1.77 mg/kg)) and a putative identification for several compounds belonging to different classes of CAs which are not included in the target list, thanks to PI survey scan." @default.
• W4283165164 created "2022-06-21" @default.
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• W4283165164 date "2022-11-01" @default.
• W4283165164 modified "2023-10-01" @default.
• W4283165164 title "Analysis of carbazole alkaloids in Murraya koenigii by means of high performance liquid chromatography coupled to Tandem mass spectrometry with a predictive multi experiment approach" @default.
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• W4283165164 doi "https://doi.org/10.1016/j.jcoa.2022.100055" @default.
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