Metabolite profiling of traditional Chinese medicine formula Dan Zhi Tablet: An integrated strategy based on UPLC-QTOF/MS combined with multivariate statistical analysis

摘要

Metabolites derived from traditional Chinese medicine (TCM) are becoming active substances of pharmacologically as well as promising sources for discovering new drugs. However, detection and identification of constituents in vivo remains a challenge for TCM, due to massive endogenous interference and low abundance of metabolites in biological matrix. Traditional Chinese medicine formula Dan Zhi Tablet (DZT), a well-established TCM formula developed based on years of clinical experiences, was widely used to treat cerebral infraction disease. In this study, an integrated strategy based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was adopted to comprehensively identify the prototype and metabolite constituents of DZT. The potential constituents were screened by cross orthogonal partial least-squares discriminant analysis (OPLS-DA). Automatic matching analysis was performed on UNIFI platform based on the function of predicting metabolites. Using this strategy, a total of 170 compounds, including 51 prototype constituents and 119 metabolites were unambiguously or tentatively identified in rat plasma. Furthermore, 31 compounds have also been detected in rat cerebrospinal fluid. The metabolism reactions included phase I reactions (hydroxylation, hydrolysis, deglycosylation, hydrogenation, demethylation and dehydroxylation) and phase 11 reactions (conjugation with glutatione, cysteine, acetylcysteine, glucuronide, sulfate). It is the first systematic metabolic study of DZT in vivo and some metabolites were also reported for the first time, which could provide a scientific basis for explaining the multiple functions of DZT. More importantly, the integrated strategy also shows promising perspectives in the identification of the metabolites in TCM from a complicated biological matrix. (C) 2018 Elsevier B.V. All rights reserved.