School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
Interdiscip Sci. 2010 Sep;2(3):221-7. doi: 10.1007/s12539-010-0026-9. Epub 2010 Jul 25.
Quantitative composition-activity relationship (QCAR) study makes it possible to discover active components in traditional Chinese medicine (TCM) and to predict the integral bioactivity by its chemical composition. In the study, 28 samples of Radix Tinosporae were quantitatively analyzed by high performance liquid chromatography, and their analgesic activities were investigated via abdominal writhing tests on mice. Three genetic algorithms (GA) based approaches including partial least square regression, radial basis function neural network, and support vector regression (SVR) were established to construct QCAR models of R. Tinosporae. The result shows that GA-SVR has the best model performance in the bioactivity prediction of R. Tinosporae; seven major components thereof were discovered to have analgesic activities, and the analgesic activities of these components were partly confirmed by subsequent abdominal writhing test. The proposed approach allows discovering active components in TCM and predicting bioactivity by its chemical composition, and is expected to be utilized as a supplementary tool for the quality control and drug discovery of TCM.
定量构效关系（QCAR）研究使得发现中药（TCM）中的活性成分并通过其化学成分预测整体生物活性成为可能。在这项研究中，采用高效液相色谱法对 28 个海风藤样本进行定量分析，并通过小鼠腹部扭体试验研究其镇痛活性。建立了三种基于遗传算法（GA）的方法，包括偏最小二乘回归、径向基函数神经网络和支持向量回归（SVR），以构建海风藤的 QCAR 模型。结果表明，GA-SVR 在海风藤生物活性预测方面具有最佳的模型性能；发现其中的七种主要成分具有镇痛活性，这些成分的镇痛活性随后通过腹部扭体试验得到部分验证。该方法可以发现中药中的活性成分，并通过其化学成分预测生物活性，有望成为中药质量控制和药物发现的补充工具。
