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J Pharm Biomed Anal · Sep 2019
Rapid profiling of alkaloid analogues in Sinomenii Caulis by an integrated characterization strategy and quantitative analysis.
- Zheng-Meng Jiang, Lan-Jin Wang, Han-Qing Pang, Yong Guo, Ping-Ting Xiao, Chu Chu, Long Guo, and E-Hu Liu.
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, PR China.
- J Pharm Biomed Anal. 2019 Sep 10; 174: 376-385.
AbstractAlkaloids, the principal constituents in the caulis of Sinomenium acutum, have gained an increasing interest over the past decades since they are widely employed as a clinical treatment for rheumatoid arthritis. In the present study, an integrated characterization strategy by combining mass defect filtering-based structure classification (MDFSC) and diagnostic fragment-ion-based extension (DFIBE) was firstly proposed for rapid profiling of alkaloids in Sinomenii Caulis (SC) via ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The rectangular MDFSC window could more accurately screen the target alkaloids of different types, and the DFIBE could facilitate the acquisition of characteristic fragment ions for structural elucidation of alkaloids. High-performance liquid chromatography (HPLC) fingerprints with principal component analysis (PCA) and hierarchical clustering analysis (HCA) was established for identifying the chemical markers and simultaneous determination of sinomenine, magnoflorine, menisperine, stepharanine and ehydrodiscretine. A total of 91 alkaloids, including 82 targeted ones (26 morphinans, 22 aporphines, 20 protoberberines and 14 benzylisoquinolines) were unambiguously identified or tentatively characterized in SC, and 14 of them were reported for the first time. Sinomenine and magnoflorine could be selected as chemical markers to evaluate the quality of SC from different localities. In conclusion, the proposed method provided a potential approach for chemical profiling and holistic quality control of herbal medicines.Copyright © 2019 Elsevier B.V. All rights reserved.
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