植物化学
体内
色谱法
化学
重复性
高效液相色谱法
萜类
传统医学
生物
生物化学
医学
生物技术
作者
Jinhong Cai,Shenghong Guan,Xueli Hu,Xuezhao Chen,Xiaosun Liu,Shouxin Li,Jingkui Tian,Ping Wang,Hua Gu,Xiaoyong Zhang
出处
期刊:Journal of AOAC International
[Oxford University Press]
日期:2024-10-14
标识
DOI:10.1093/jaoacint/qsae079
摘要
Abstract Background Premna microphylla Turcz. (PMT) is a traditional food and medicinal plant, which has been used to treat cure hemostasis, rheumatism and dysentery. However, it still lacks a clear understanding about chemical profile of PMT and metabolites in vivo. Objective To establish a rapid and efficient analytical method for the identification of phytochemical in PMT and metabolites in vivo. Methods Firstly, the fingerprint of PMT was established by high-performance liquid chromatography (HPLC) with methodology validation. Then, the phytochemical composition in PMT leaves were identified using ultra-performance liquid chromatography tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF-MS/MS). Finally, the prototype and correlated metabolites were detected after oral administration in mice to understand the absorption and metabolism of phytochemical in vivo. Results The result showed established HPLC method for fingerprints evaluation of PMT has good precision, repeatability and stability. Additionally, a total of 103 phytochemicals were identified in PMT, mainly including flavonoids and terpenoids. Then, 37 prototype components and 20 derived metabolites in vivo were detected. Conclusion In this study, we constructed a fingerprint method which has good stability, precision and repeatability, and the fingerprint of PMT was established. Then the chemical profile of PMT in vitro and in vivo was performed. The result showed that flavonoids and terpenoids were the main phytochemicals in PMT, and methylation, sulfonation, dihydroxylation were the main metabolic pathway in vivo. Highlights The present study provided the phytochemical basis for the subsequent study of pharmacological activity.
科研通智能强力驱动
Strongly Powered by AbleSci AI