Journal
JOURNAL OF PHARMACEUTICAL ANALYSIS
Volume 12, Issue 1, Pages 136-144Publisher
ELSEVIER
DOI: 10.1016/j.jpha.2021.02.003
Keywords
Metabolic profiles; Extension-mass defect filter; Multidimensional data acquiring; Metabolic network prediction; Bufadienolides of Venenum Bufonis
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Funding
- National Natural Science Foundation of China [81530095, 81673591]
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA12020348]
- National Standardization of Traditional Chinese Medicine Project [ZYBZH-K-LN-01]
- Science and Technology Commission Foundation of Shanghai [15DZ0502800]
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This study established an effective strategy to comprehensively characterize the metabolites of bufadienolides in vivo, deduce the metabolic pathways, and describe the metabolic profiles. The strategy successfully identified 147 metabolites and elucidated the main metabolic pathways of bufadienolides in vivo.
Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable sig-nificance in determining the efficacy and safety of traditional Chinese medicine (TCM) in vivo. However, this process is usually hindered by the insufficient characteristic fragments of metabolites, ubiquitous matrix interference, and complicated screening and identification procedures for metabolites. In this study, an effective strategy was established to systematically characterize the metabolites, deduce the metabolic pathways, and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo. The strategy was divided into five steps. First, the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry (MS) system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites. Second, an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites, which reduced approximately 39% of the interfering ions. Third, an acquisition model was developed and used to trigger more tandem MS (MS/ MS) fragments of precursor ions based on the targeted ion list. The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode. Fourth, the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction. The main in vivo metabolic pathways of bufadienolides were elucidated. A total of 147 metabolites were characterized, and the main biotransformation reactions of bufadienolides were hydroxylation, dihydroxylation, and isomerization. Finally, the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS, and the metabolic profiles were clearly identified. This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use. (c) 2021 Xi'an Jiaotong University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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