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

Jianlan Jiang; Qingjie Tan; Weifeng Li; Xinyun Du; Ningzhi Liu

首页> 中文期刊> 《天津大学学报：英文版》 >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

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

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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.

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《天津大学学报：英文版》 |2017年第3期|237-244|共8页
作者
Jianlan Jiang; Qingjie Tan; Weifeng Li; Xinyun Du; Ningzhi Liu;
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作者单位

Key Laboratory of Systems Bioengineering;

Ministry of Education;

Tianjin Key Laboratory of Biological and Pharmaceutical Engineering;

School of Chemical Engineering and Technology;

Tianjin University;

Tianjin 300072;

China;

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原文格式 PDF
正文语种 chi
中图分类高等教育;
关键词
Curcuma; longa; L.; Glycyrrhiza; Active; compound; identification; Support; vector; regression; Mean; impact; value;

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

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