基于组效关系的姜黄挥发油抗肿瘤活性成分辨识研究

基于GC-MS分析,建立了姜黄挥发油的化学成分与其抑制人宫颈癌Hela细胞作用的组效关系模型,寻找与药效显著相关的活性成分。采用水蒸汽蒸馏法提取得到31批姜黄挥发油,提取率在1.63%～4.52%之间;采用GC-MS联用仪建立了31批姜黄挥发油的指纹图谱,确定了20个特征峰,以特征峰的相对峰面积(各峰面积与内标正十三烷的峰面积之比)来表征其相对含量;MTT法测定姜黄挥发油抑制人宫颈癌Hela细胞活性,以抑制率为评价指标;利用Simca-p11.5软件的正交投影偏最小二乘法(Orthogonal Partial leastsquares,OPLS)和SPSS软件的双变量相关(bivariate)分析,研究特征峰与药效的相关性,根据S-载荷图、变异权重参数值(Variable importance in projection,VIP)和皮尔逊(Pearson)相关系数来辨识显著活性成分。结果表明,11,15,7,19,3,6,12,14,9号等9个特征峰与姜黄挥发油抑制Hela细胞活性显著相关,除19号峰尚未定性外,11,15,7,3,6,12,14和9号峰对应的成分分别为芳姜黄酮、β-姜黄酮、姜烯、β-榄香烯、α-姜黄烯、α-姜黄酮、吉马酮和β-倍半水芹烯。

Identification of Anti-tumor Ingredients in Curcuma Volatile Oil Based on Composition-Activity Relationship

The Composition-activity relationship(CAR) model of curcuma volatile oil based on GC-MS analysis was established to recognize the active compounds.31 batches of curcuma volatile oil were prepared using steam distillation with the extraction rate ranging from 1.63% to 4.52%,and quantitatively analyzed by GC-MS.Anti-tumor activity was investigated by MTT assays on Hela cell line.The orthogonal partial least squares(OPLS) and bivariate correlation analysis was respectively performed on SIMCA-P 11.5 and SPSS 18.0 software to construct the CAR model of curcuma volatile oil.The results showed that 9 peaks including Peaks 11,15,7,19,3,6,12,14 and 9 were significantly related to anti-tumor activity according to scores plot,variable importance in projection(VIP) values in OPLS and Pearson correlation coefficient in bivariate correlation analysis;Peaks 11,15,7,3,6,12,14 and 9 were identified as ar-tumerone,β-tumerone,zingiberene,β-elemene,α-curcumene,α-tumerone,germacrone,β-sesquiphellandrene,respectively.