A database-guided integrated strategy for comprehensive chemical profiling of traditional Chinese medicine.
J Chromatogr A
; 1674: 463145, 2022 Jul 05.
Article
em En
| MEDLINE
| ID: mdl-35594798
ABSTRACT
A comprehensive chemical profiling of traditional Chinese medicine is the basic issue for further pharmacological research and quality assessment. To facilitate chemical identification and potential components discovery, the present study proposed an integrated identification strategy guided by a self-built component database constructed from literatures to carry out the global profiling of complex matrixes. Lanqin Oral Liquid was applied as example to validate the feasibility of this strategy. Based on LQL Component Database containing 710 compounds, modified MDF windows was established to extract the interested analogues, isoquinoline alkaloids, flavonoids and iridoid glycosides, according to their regular integral masses and mass defect. For compounds with characteristic substructures, such as quinic acids, crocins and some glycoside derivatives, the associated neutral losses and diagnostic fragment ions were collected to assist in profiling. Directly matching the m/z or formulas in database was proposed to components with limited regularity of accurate masses and substructures, like indole alkaloids, sesquiterpenes and some nucleosides. Eventually, 170 ions of 1038 precursor ions were identified or temporarily deduced, including 59 alkaloids, 36 flavonoids, 48 terpenoids, 24 organic acids and their derivatives, 2 oligosaccharides, and 1 lignans. Among them, 52 putative compounds were confirmed by chemical standards. The results indicated that the database-oriented identification strategy could locate potential components quickly and eliminate interfering ions, which have the potential for in-depth analysis of compounds.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Medicamentos de Ervas Chinesas
/
Alcaloides
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article