Metabolite profiling plays a crucial role in drug discovery and development, and HPLC-Q-TOF has evolved into a powerful and effective high-resolution analytical tool for metabolite detection. However, traditional empirical identification is laborious and incomplete. This paper presents a systematic and comprehensive strategy for elucidating metabolite structures using software-assisted HPLC-Q-TOF that takes full advantage of data acquisition, data processing, and data mining technologies, especially for high-throughput metabolite screening. This strategy has been successfully applied in the study of magnoflorine metabolism based on our previous report of its poor bioavailability and drug-drug interactions. In this report, 23 metabolites of magnoflorine were tentatively identified with detailed fragmentation pathways in rat biological samples (urine, feces, plasma, and various organs) after i.p. or i.g. administration, and for most of these metabolites, the metabolic sites were determined. The phase I biotransformations of magnoflorine (M1-M7, M10-M14) consist of demethylation, dehydrogenation, hydroxylation, methylene to ketone transformation, N-ring opening, and dehydroxylation. The phase II biotransformations (M8, M9, and M15-M23) consist of methylation, acetylation, glucuronidation, and N-acetylcysteine conjugation. The results indicate that the extensive metabolism and wide tissue distribution of magnoflorine and its metabolites may partly contribute to its poor bioavailability and drug-drug interaction, and i.p. administration should thus be a suitable approach for isolating magnoflorine metabolites. In summary, this strategy could provide an efficient, rapid, and reliable method for the structural characterization of drug metabolites and may be applicable for general Q-TOF users.
Keywords: High resolution; LC-Q-TOF; Magnoflorine; Metabolite ID; Metabolites.