Interpreting metabolic profiling of YIV906 in vivo: A Synergistic strategy combining LC-HRMS-based molecular...

The components can be transformed into various metabolites that exert pharmacological effects under the action of gut microbiota and drug-metabolizing enzymes after administration of traditional Chinese medicine (TCM). However, the comprehensive characterization of herbal compounds in vivo is still an important challenge due to the interference of endogenous compounds. In this study, an integrated strategy was built to illustrate the metabolic profiling of YIV906 (PHY906), a standardized botanical cancer drug candidate originated from Huangqin Tang in vivo by combining multivariate statistical analysis and feature based molecular networking (FBMN) based on UHPLC-Q-Exactive orbitrap mass spectrometry. Firstly, a multivariate statistical analysis method was used to screen differential components on account of metabolomics approach. Meanwhile, the self-build database was built to preliminarily screen prototype components and then operated in the Compound Discoverer software to accelerate the discovery of metabolites based on Biotransformation module. Furthermore, a FBMN was constructed on the Global Natural Product Social (GNPS) platform to accurately annotate compound structures and infer unknown metabolites according to the differences of MS² spectra and m/z value with adjacent nodes. Finally, a total of 63 prototype components and 180 metabolites including flavonoid, organic acid, monoterpenoid, triterpenoid and alkaloid were successfully identified in rat plasma and tissues based on the established strategy, as well as revealed the tissue distribution characteristics of main components. In conclusion, this study demonstrated an effective and practical strategy that enables the in-depth metabolic profiling of complex biological samples.