Improved structural annotation of triterpene metabolites of traditional Chinese medicine in vivo based on quantitative structure-retention relationships combined with characteristic ions: Alismatis Rhizoma as an example

As a fast, sensitive and selective method, liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) has been used for studying the in vivo metabolism of traditional Chinese medicine (TCM). However, the rapid discovery and characterization of metabolites, especially isomers, remain challenging due to their complexity and low concentration in vivo. This study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a quantitative structure-retention relationship (QSRR) model, and Alismatis Rhizoma (AR) triterpenes were used as an example. This strategy consists of four steps. First, based on an in-house database reported previously, prototypes and metabolites in biosamples were preliminarily identified. Second, the candidate structures of prototype compounds and metabolites were determined by characteristic ions, databases or potential metabolic pathways. Then, a QSRR model was established to predict the retention times of the proposed structure. Finally, the structures of unknown prototypes and metabolites were determined by matching experimental retention times with the predicted values. The QSRR model built by the genetic algorithm-multiple linear regression (GA-MLR) has excellent regression correlation (R-2 = 0.9966). Based on this strategy, a total of 118 compounds were identified, including 47 prototypes and 71 metabolites, among which 61 unknown compounds were reasonably characterized. The typical compound identified by this strategy was successfully validated using a triterpene standard. This strategy can improve the annotation confidence of in vivo metabolites of TCM and facilitate further pharmacological research.

Automated summary

The study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a QSRR model, identifying a total of 118 compounds, including 47 prototypes and 71 metabolites, with 61 unknown compounds reasonably characterized. The QSRR model built by GA-MLR showed excellent regression correlation (R-2 = 0.9966), demonstrating the effectiveness of the strategy in enhancing the annotation confidence of in vivo metabolites of TCM.