Comparative identification of the metabolites of dehydrocorydaline from rat plasma, bile, urine and feces by both the targeted and untargeted liquid chromatography/mass spectrometry strategies

Arabian Journal of Chemistry(2022)

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摘要
Severe interference from the endogenous substances is often encountered in characterizing the drug metabolites by liquid chromatography/mass spectrometry using data-dependent acquisition (DDA). To add a precursor ions list (PIL) by DDA or apply data-independent acquisition (DIA) coupled with post-acquisition data processing (such as mass defect filtering, MDF) may assist to target more metabolites from the complex biosamples. Dehydrocorynine (DHC) is a bioactive alkaloid compound rich in Corydalis yanhusuo. We integrated both PIL-DDA and DIA-MDF strategies to probe the metabolites of DHC simultaneously from the rat plasma, bile, urine, and feces. Chromatographic separation was performed on an HSS C18 SB column. The positive-mode collision-induced dissociation-MS2 data of DHC metabolites were recorded by PIL-DDA on both the QTrap 4500 and Vion IM-QTOF mass spectrometers, and by HDMSE on Vion IM-QTOF. Efficient workflows to process the high-definition DDA (HDDDA) and HDMSE data were elaborated. Totally 40 metabolites (orally administrated at the dose of 100 mg/kg) were identified or tentatively characterized, involving 30 from bile, 16 from feces, 7 from plasma, and 18 from urine. The methoxyls and C-5/C-6/C-8 were the main sites prone to be metabolized via demethylation and oxidation, and further glucuronic acid conjugation and sulfuric acid conjugation. Compared with literature, we can newly discover 17 metabolites in bile, and, for the first time, report the metabolites of DHC from rat urine and feces. Conclusively, the presented PIL-DDA and DIA-MDF strategies are powerful in elucidating the drug metabolites, which thus provides reference to characterizing the metabolic profiles of traditional Chinese medicine components.
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关键词
Dehydrocorynine,Precursor ions list,Data-dependent acquisition,Data-independent acquisition,Mass defect filtering,Metabolite characterization
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