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.
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 / Viteritti, Eduardo; Oliva, Eleonora; Eugelio, Fabiola; Fanti, Federico; Palmieri, Sara; Bafile, Eleonora; Compagnone, Dario; Sergi, Manuel. - In: JOURNAL OF CHROMATOGRAPHY OPEN. - ISSN 2772-3917. - 2:(2022), pp. 1-10. [10.1016/j.jcoa.2022.100055]
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
Oliva, Eleonora;Compagnone, Dario;Sergi, Manuel
2022
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.File | Dimensione | Formato | |
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