Hybrid multidimensional data acquisition and data processing strategy for comprehensive characterization of known, unknown and isomeric compounds from the compound Dan Zhi Tablet by UPLC-TWIMS-QTOFMS.
RSC Adv
; 9(16): 8714-8727, 2019 Mar 15.
Article
em En
| MEDLINE
| ID: mdl-35517662
ABSTRACT
The compound Dan Zhi Tablet (DZT), a reputable traditional Chinese medicine prescription, is widely used for the treatment of ischemic stroke in clinic. However, its systematic chemical constituents have rarely been elucidated, which hampers its quality evaluation, the study of bioactive constituents and the mechanism of action interpretation. In this study, we developed a combination of multidimensional data acquisition and data processing strategy with the aim to globally and comprehensively identify the chemical constituents in DZT based on UPLC-TWIMS-QTOFMS. First, multidimensional acquisition modes (MSE, Fast DDA and HDMSE) were performed on UPLC-TWIMS-QTOFMS. Second, targeted characterizations of the known compounds and their analogues present in DZT were carried out on the basis of the corresponding commercial standards or Mass2Motifs. Third, untargeted identification of unknown compounds in DZT was performed by extracting shared Mass2Motifs from the raw fragmentation spectra. Finally, the coeluting isomers were characterized using a precursor and/or product ion mobility. Consequently, 202 compounds were detected from DZT 29 of them were unambiguously identified by comparison with reference compounds, 29 unknown compounds were discovered in specific medicinal materials, and ten pairs of coeluting isomers, which could not be distinguished using conventional MSE or Fast-DDA, were resolved using HDMSE only. This strategy was successfully used for the rapid and global identification of complex compounds including known, unknown and coeluting isomeric compounds in DZT and provided helpful chemical information for further quality control, pharmacology and active mechanism research on DZT.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Guideline
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article