A tailored database combining reference compound-derived metabolite, metabolism platform and chemical characteristic of Chinese herb followed by activity screening: Application to Magnoliae Officinalis Cortex

J Pharm Anal. 2025 Apr;15(4):101066. doi: 10.1016/j.jpha.2024.101066. Epub 2024 Aug 8.

ABSTRACT

A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex (MOC) was developed and implemented to rapidly profile and discover bioactive metabolites in vivo derived from traditional Chinese medicine (TCM). The strategy possessed four characteristics: 1) The tailored database consisted of metabolites derived from big data-originated reference compound, metabolites predicted in silico, and MOC chemical profile-based pseudomolecular ions. 2) When profiling MOC-derived metabolites in vivo, attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds, as reported by most papers, but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites. 3) Metabolite traceability was performed, especially to distinguish isomeric prototypes-derived metabolites, prototypes of MOC compounds as well as phase I metabolites derived from other MOC compounds. 4) Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification. Using this strategy, 134 metabolites were swiftly characterized after the oral administration of MOC to rats, and several metabolites were reported for the first time. Furthermore, 17 potential active metabolites were discovered by targeting the motilin, dopamine D2, and the serotonin type 4 (5-HT₄) receptors, and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model. This study extends the application of mass spectrometry (MS) to rapidly profile TCM-derived metabolites in vivo, which will help pharmacologists rapidly discover potent metabolites from a complex matrix.

PMID:40329977 | PMC:PMC12053571 | DOI:10.1016/j.jpha.2024.101066