Rapid and accurate metabolite identification of traditional Chinese medicine based on UPLC-Q-TOF-MS coupled with UNIFI analysis platform and quantitative structure-retention relationship: Danshen-Honghua herbal pair as an example

Abstract

In recent years, metabolite identification of chemical constituents of traditional Chinese medicine (TCM) has been extensively studied. However, due to the intricacy of metabolic processes and the low concentration of metabolites, identifying metabolites of TCM in vivo is still a tough work. Meanwhile, credibility of metabolite identification through mass spectrum technology has been called into question by reason of the lack of metabolite standards. In this study, ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) was used to detect biological samples including plasma, feces, urine, liver, kidney, brain of normal and middle cerebral artery occlusion (MCAO) rats orally administrated water extract of Danshen-Honghua herbal pair (DHHP). An analysis strategy which combined MS data analysis platform UNIFI with quantitative structure-retention relationship (QSRR) model was established. First, metabolites of DHHP were identified rapidly by utilizing UNIFI analysis platform to analyze acquired MS data. Then, quantitative structure-retention relationships model was built through BP neural network optimized by the ant colony algorithm. Finally, predicted retention times of identified metabolites were produced by QSRR model. Metabolites identified by UNIFI whose difference between predicted and experimental retention time was beyond 1 min were considered false positive and excluded to improve the credibility of identification. According to the established analysis strategy, 26 prototypes and 16 metabolites were identified. Established MS data analysis strategy which combined UNIFI analysis platform with QSRR model was proven to be a creditable method to identify the in vivo metabolites of TCM rapidly and accurately.