Rapid identification of chemical profiles in vitro and in vivo of Huan Shao Dan and potential anti-aging metabolites by high-resolution mass spectrometry, sequential metabolism, and deep learning model - Li, Xueyan; Pan, Fulu; Wang, Lin; Zhang, Jing; Wang, Xinyu; Qi, Dongying; Chai, Xiaoyu; Wang, Qianqian; Yi, Zirong; Ma, Yuming; Pan, Yanli; Liu, Yang; et al. (Frontiers in Pharmacology, 2024)
Background: Aging, is marked by the gradual deterioration of cells, tissues, and organs and is a major risk factor for many chronic diseases. Considering the complex mechanisms of aging, TCM could offer distinct advantages. However, due to the complexity and variability of metabolites in TCM, comprehensive screening of metabolites associated with pharmacological remains a significant issue. Methods: First, a reliable and integrated identification method based on UPLC-Q Exactive-Orbitrap HRMS was established to identify chemical profiles of HSD. Second, based on the theory of sequential metabolism, metabolic sites of HSD in vivo were further investigated. Finally, a deep learning model and bioactivity assessment assay were applied to screen potential anti-aging metabolites. Results: In this study, a total of 366 metabolites were identified in HSD. Then, based on the result of sequential metabolism, 135 metabolites were absorbed into plasma. 178 peaks were identified from the sample after incubation with the artificial gastric juice. In addition, 102 and 91 peaks were identified from the feces and urine samples. Finally, based on the results of the deep learning model and bioactivity assay, Ginsenoside Rg1, Ginsenoside Rg2, Ginsenoside Rc, Pseudoginsenoside F11, and Jionoside B1 were selected as potential anti-aging metabolites. Conclusion: This study provided a valuable reference for future research on the material basis of HSD by describing the chemical profiles both in vivo and in vitro. Moreover, the proposed screening approach may serve as a rapid tool for identifying potential anti-aging metabolites in TCM.