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

• Published in: Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Analytical technologies in the biomedical and life sciences, 2021-12, Vol.1187, p.123012-123012, Article 123012
• Main Authors: Yan, Pan, Wang, Lu, Li, Sen, Liu, Xuechen, Sun, Yi, Tao, Jianmei, Ouyang, Hui, Zhang, Jianqing, Du, Zhifeng, Jiang, Hongliang
• Format: Article
• Language: English
• Subjects: Alismataceae - chemistry; Alismatis Rhizoma; Animals; Characteristic ions; Chromatography, High Pressure Liquid; Drugs, Chinese Herbal - chemistry; Drugs, Chinese Herbal - pharmacokinetics; Feces - chemistry; Male; Medicine, Chinese Traditional; Metabolites; Quantitative Structure-Activity Relationship; Quantitative structure-retention relationship; Rats; Rats, Sprague-Dawley; Rhizome - chemistry; Tandem Mass Spectrometry; Triterpenes - analysis; Triterpenes - chemistry; Triterpenes - metabolism
• ISSN: 1570-0232; 1873-376X
• DOI: 10.1016/j.jchromb.2021.123012

•A strategy was proposed to annotate metabolites of traditional Chinese medicine.•The candidate structures of prototype compounds and metabolites were determined.•A QSRR model was established to predict the retention time of candidate structures.•61 unknown prototypes and in vivo metabolites of AR triterpenes were annotated.•Typical compound identified by this strategy was successfully validated using triterpene standard. 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 (R2 = 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.