Authors: Rong Xing, Lijun Zhou, Lin Xie, Kun Hao, Tai Rao, Qian Wang, Wei Ye, Hanxu Fu, Xinwen Wang, Guangji Wang, Yan Liang

PMID: 25813028

DOI: 10.1016/j.aca.2015.02.039

Publication Type: Research Support, Non-U.S. Gov’t

ISSN: 1873-4324

Journal Title: Analytica chimica acta

Publication Date: 2015 Mar 31

Abstract

The present work contributes to the development of a powerful technical platform to rapidly identify and classify complicated components and metabolites for traditional Chinese medicines. In this process, notoginsenosides, the main active ingredients in Panaxnotoginseng, were chosen as model compounds. Firstly, the fragmental patterns, diagnostic product ions and neutral loss of each subfamily of notoginsenosides were summarized by collision-induced dissociation analysis of representative authentic standards. Next, in order to maximally cover low-concentration components which could otherwise be omitted from previous diagnostic fragment-ion method using only single product ion of notoginsenosides, a multiple product ions filtering strategy was proposed and utilized to identify and classify both non-target and target notoginsenosides of P.notoginseng extract (in vitro). With this strategy, 13 protopanaxadiol-type notoginsenosides and 30 protopanaxatriol-type notoginsenosides were efficiently extracted. Then, a neutral loss filtering technique was employed to trace prototype components and metabolites in rats (in vivo) since diagnostic product ions might shift therefore become unpredictable when metabolic reactions occurred on the mother skeleton of notoginsenosides. After comparing the constitute profiles in vitro with in vivo, 62 drug-related components were identified from rat feces, and these components were classified into 27 prototype compounds and 35 metabolites. Lastly, all the metabolites were successfully correlated to their parent compounds based on chemicalome-metabolome matching approach which was previously built by our group. This study provided a generally applicable approach to global metabolite identification for the complicated components in complex matrices.

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