期刊文献+

Use of Support Vector Regression Based on Mean Impact Value Model to Identify Active Compounds in a Combination of Curcuma longa L.and Glycyrrhiza extracts 被引量:3

Use of Support Vector Regression Based on Mean Impact Value Model to Identify Active Compounds in a Combination of Curcuma longa L. and Glycyrrhiza extracts
下载PDF
导出
摘要 A support vector regression based on the mean impact value (MIV) model was constructed to identify the bioactive compounds inhibiting proliferation of HeLa cells in a combination of turmeric (Curcuma longa L.) and liquorice (Glycyrrhiza) extracts. The quantitative chemical fingerprint from 50 batches of turmeric and liquorice extracts was established using high performance liquid chromatography hyphenated to an ultraviolet visible detector. Qualitative results were obtained using ultra performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry. A total of 46 peaks (peaks 1–15 from turmeric and 16–46 from liquorice) were selected as “common peaks” for analysis. The inhibitory effect of the combined extracts on HeLa cells was measured by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. It was found that 15 compounds (peaks: 8, 12, 30, 24, 46, 11, 14, 9, 3, 1, 44, 18, 7, 45 and 43) possessing high absolute MIV exhibited a significant correlation with the cytotoxicity against HeLa cells; most of these have already been confirmed with potential cytotoxicity in previous research. The important potential application of the present model can be extended to help discover active compounds from complex herbal medicine prior to traditional bioassay-guided separation. It is considered that this could be a useful tool for re-developing herbal medicine based on the use of these active compounds. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg. A support vector regression based on the mean impact value (MIV) model was constructed to identify the bioactive compounds inhibiting proliferation of He La cells in a combination of turmeric (Curcuma longa L.)and liquorice (Glycyrrhiza) extracts.The quantitative chemical fingerprint from 50 batches of turmeric and liquorice extracts was established using high performance liquid chromatography hyphenated to an ultraviolet visible detector.Qualitative results were obtained using ultra performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry.A total of 46 peaks (peaks 1–15 from turmeric and 16–46 from liquorice) were selected as "common peaks" for analysis.The inhibitory effect of the combined extracts on He La cells was measured by MTT (3- (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay.It was found that 15 compounds (peaks:8,12,30,24,46,11,14,9,3,1,44,18,7,45 and 43)possessing high absolute MIV exhibited a significant correlation with the cytotoxicity against He La cells; most of these have already been confirmed with potential cytotoxicity in previous research.The important potential application of the present model can be extended to help discover active compounds from complex herbal medicine prior to traditional bioassay-guided separation.It is considered that this could be a useful tool for redeveloping herbal medicine based on the use of these active compounds.
出处 《Transactions of Tianjin University》 EI CAS 2017年第3期237-244,共8页 天津大学学报(英文版)
基金 financial support provided by the National Natural Science Foundation of China(No.81102900)
关键词 BIOASSAY Electrospray ionization Food products High performance liquid chromatography Ionization of liquids Liquid chromatography Mass spectrometry Medicine Plant extracts Regression analysis Curcuma longa L. Glycyrrhiza Active compound identification Support vector regression Mean impact value
  • 相关文献

参考文献1

二级参考文献22

  • 1唐课文,陈国斌.气相色谱-质谱法分析姜黄挥发油化学成分[J].质谱学报,2004,25(3):163-165. 被引量:10
  • 2王海晶,杨和平.姜黄挥发油对人肺腺癌A549细胞作用的形态学研究[J].第三军医大学学报,2005,27(3):220-222. 被引量:14
  • 3Li M, Zhou X, Zhao Y, Wang D P, Hu X N. Bull. Korean Chem. Soc., 2009, 30(10): 2287-2293.
  • 4Cheng Y Y, Wang Y, Wang X W. Comput Bio. Chem., 2006, 30(2): 148-154.
  • 5Yan S K, Lin Z Y, Dai W X, Shi Q R, Liu X H, Jin H Z, Zhang W D. Interdiscip Sci. Comput. Llife Sci., 2010, 2(3): 221-227.
  • 6Chen Y, Wu C M, Dai R J, Li L, Yu Y H, Li Y, Meng W W, Zhang L, Zhang Y Q, Deng Y L. J. Chromatogr. B, 2011, 879(5): 371-378.
  • 7Bylesjo M, Rantalainen M, Cloarec O, Nicholson J K, Holmes E, Trygg J. J. Chemom., 2006, 20(8): 341-351.
  • 8Wiklund S, Johansson E, Sjostrom L, Mellerowicz E J, Edlund U, Shockcor J P, Gottfries J, Moritz T, Trygg J. Anal. Chem., 2008, 80(1): 115-122.
  • 9Zhou X, Li Z W, Liang G Y, Zhou J, Wang D P, Cai Z W. J. Pharm. Biomed. Anal., 2007, 43(2): 440-444.
  • 10Qin N Y, Yang F Q, Wang Y T, Li S P. J. Pharm. Biomed. Anal., 2007, 43(2): 486-492.

共引文献28

同被引文献45

引证文献3

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部