ABSTRACT
To elucidate the chemical composition of a traditional Chinese medicine (TCM) formula necessitates the development of more potent analytical strategies, because of the complexity as a result of the superposition of multiple drugs. Fangji Huangqi Decoction (FHD) is a fourcomponent TCM formula composed by complicated chemical constituents. By using the VionTM ion mobility quadrupole time-of-flight (IM-QTOF) mass spectrometer, we present a novel IM separation-enabled and precursor ions list (PIL)-included high-definition data-dependent acquisition (HDDDA) approach, and apply it to the multicomponent characterization of FHD by cou-pling to ultra-high performance liquid chromatography.
Chromatographic separation was conducted on a CORTECS UPLC T3 column, while HDDDA was employed for MS2 data acquisition in both the negative and positive electrospray-ionization (ESI) modes. The PILs of FHD in two ESI modes were created based on the phytochemical knowledge of four component drugs and mass defect filtering, which ultimately could obtain 316 and 258 targeted masses, respectively, from the full-scan MS1 data. Interestingly, the additional inclusion of PILs in HDDDA could improve the coverage on the target components by 12% (ESI–) and 48% (ESI + ). Structural elucidation was performed by in-house database-driven automatic peak annotation using UNIFITM. We could identify or tentatively characterize 203 components from FHD, involving 25 alkaloids, 86 flavonoids, 48 triterpenoids (saponins), 16 lactones, and 28 others). It is the first report regarding the method development of HDDDA that targets the global TCM components characterization, and the findings obtained would benefit the quality control and the secondary development of FHD.
Reagents and chemicals
Chengdu Desite Biotechnology Co., Ltd. (Chengdu, China), were used as the reference compounds in this work.
原文链接:http://creativecommons.org/licenses/by-nc-nd/4.0/
Royal society of chemistry-12 Mar 2020
The development and
World Journal of Microbiology and Biotechnology-20 June 2018
期刊名:World Journal of
Nat Biotech| 北京大学谢正伟课题组与合作者创建基于基因指纹和深度学习的药效预测系统(DLEPS)
原文链接:htt
Journal of separation science-26 November 2019
Biosurfactant trehal
Journal of separation science-07 January 2020
Comparison of the ac
细辛水提物HPLC指纹图谱及化学模式识别的研究
摘要:[目的] 通过建立中药细辛水提物高