A Systematic Strategy for the Characterization of 2,3,5,4'-Tetrahydroxystilbene-2- O -β-d-glucoside Metabolites In Vivo by Ultrahigh Performance Liquid Chromatography Coupled with a Q Exactive-Orbitrap Mass System.
Juan HuangShuyi HuangJing ZhangYouling LiangJunqi BaiWen XuLu GongHe SuZhihai HuangXiaohui QiuPublished in: Journal of agricultural and food chemistry (2022)
2,3,5,4'-Tetrahydroxystilbene-2- O -β-d-glucoside (THSG), a polyphenol stilbene compound, is the main active constituent in Polygonum multiflorum . In this study, a comprehensive analytical strategy was developed for the characterization of THSG metabolites in vivo (rat plasma, bile, urine, heart, liver, spleen, lung, kidney, and stomach) utilizing ultrahigh performance liquid chromatography coupled with Q Exactive hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q Exactive-Orbitrap MS) based on multiple data-processing techniques. As a result, a total of 75 metabolites were characterized in bio-samples, and calculated Clog P values were further employed to assign the chemical structures of some isomers. Glucoside hydrolysis, hydrogenation, hydroxylation, glucuronide conjugation, and sulfate conjugation would be the major metabolic pathways of THSG. It appeared that most metabolites would generally undergo phase I reactions followed by phase II reactions. These results provided valuable information for in-depth understanding of the safety and efficacy of THSG and showed a valuable methodology for metabolic characterization.
Keyphrases
- liquid chromatography
- mass spectrometry
- ultra high performance liquid chromatography
- tandem mass spectrometry
- high resolution mass spectrometry
- ms ms
- simultaneous determination
- gas chromatography
- high performance liquid chromatography
- phase ii
- high resolution
- solid phase extraction
- clinical trial
- capillary electrophoresis
- open label
- atrial fibrillation
- heart failure
- optical coherence tomography
- randomized controlled trial
- electronic health record
- big data
- deep learning
- anaerobic digestion
- multiple sclerosis
- social media
- artificial intelligence
- machine learning