| Literature DB >> 34277119 |
Chanjuan Yu1, Fengyun Wang2, Xinyue Liu1, Jiayan Miao1, Siqi Tang1, Qin Jiang1, Xudong Tang2, Xiaoyan Gao1.
Abstract
Deciphering the metabolites of multiple components in herbal medicine has far-reaching significance for revealing pharmacodynamic ingredients. However, most chemical components of herbal medicine are secondary metabolites with low content whose in vivo metabolites are close to trace amounts, making it difficult to achieve comprehensive detection and identification. In this paper, an efficient strategy was proposed: herb-derived metabolites were predicted according to the structural characteristics and metabolic reactions of chemical constituents in Corydalis Rhizoma and chemical structure screening tables for metabolites were conducted. The fragmentation patterns were summarized from representative standards combining with specific cleavage behaviors to deduce structures of metabolites. Ion abundance plays an important role in compound identification, and high ion abundance can improve identification accuracy. The types of metabolites in different biological samples were very similar, but their ion abundance might be different. Therefore, for trace metabolites in biological samples, we used the following two methods to process: metabolites of high dose herbal extract were analyzed to characterize those of clinical dose herbal extracts in the same biological samples; cross-mapping of different biological samples was applied to identify trace metabolites based on the fact that a metabolite has different ion abundance in different biological samples. Compared with not using this strategy, 44 more metabolites of clinical dose herbal extract were detected. This study improved the depth, breadth, and accuracy of current methods for herb-derived metabolites characterization.Entities:
Keywords: Alkaloid; Characteristic fragment; Corydalis yanhusuo; In vivo metabolism; Metabolite
Year: 2020 PMID: 34277119 PMCID: PMC8264384 DOI: 10.1016/j.jpha.2020.03.006
Source DB: PubMed Journal: J Pharm Anal ISSN: 2214-0883
Fig. 1Summary diagram of proposed analytical strategy for identification of Yanhusuo alkaloids metabolites.
Fig. 2Proposed metabolic pathway of tetrahydroberberine (A), tetrahydropalmatine (B), palmatine (C), and protopine (D).
The metabolic characteristic fragments (MCFs) of Yanhusuo-derived metabolites.
| Type | Standards | MCFs | |
|---|---|---|---|
| Product ions | Neutral loss (Da) | ||
| C6H8O6 (176.0321) | |||
| C6H8O6 (176.0321) | |||
| [M-CH3]+ | CH3 (15.0235) | ||
| [M-(CH3)2NH]+ | (CH3)2NH (45.0578) | ||
The chemical structure screening table of tetrahydroprotoberberine alkaloids.
Red numbers indicated quasi-molecular ions matched with Yanhusuo metabol2ites.
The chemical structure screening table of protopine alkaloids.
Red numbers indicated quasi-molecular ions matched with Yanhusuo metabolites.
The chemical structure screening table of protoberberine alkaloids.
Red numbers indicated quasi-molecular ions matched with Yanhusuo metabolites.
Fig. 3The chromatograms of clinical Yanhusuo extract administration plasma sample: (A) the extract ion chromatogram of m/z 504.1881, and (B) the total ion chromatogram of plasma sample.
Fig. 4The MS/MS spectra and structure of MN-100.
Fig. 5The extract ion chromatograms (A) and MS/MS spectra (B) of m/z 518.2028 in plasma samples of clinical dose and high dose extracts.
Fig. 6The extract ion chromatograms of m/z 342.1715: (A) clinical dose plasma sample, (B) high dose plasma sample, and (C) clinical dose urine sample.
Fig. 7The MS/MS spectra and structures of MN-28 and MN-29.
Fig. 8The relative percentage of metabolite types in four reference standards.