Fan Hailiu, Xuan Jianbang, Du Xinyun, Liu Ningzhi, Jiang Jianlan
Key Laboratory of Systems Bioengineering, Ministry of Education, School of Chemical Engineering and Technology, Tianjin University Tianjin 300072 P. R. China
RSC Adv. 2018 Nov 27;8(69):39602-39610. doi: 10.1039/c8ra07911k. eCollection 2018 Nov 23.
The purpose of this research is to recognize the active antitumor components from the mixed pair extract of Aconiti Lateralis Radix Praeparata (Fuzi in Chinese) and Glycyrrhizae Radix et Rhizoma (Gancao in Chinese) using chemometrics and mean impact value (MIV) methods. Firstly, 30 common components of 31 different samples were analyzed quantitatively and qualitatively by HPLC-UV and UPLC-Q-TOF tandem mass spectrometry, respectively. Meanwhile, MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays were used to test the inhibition activities of the 31 different samples against HeLa cells. Then a back propagation (BP) neural network, support vector regression (SVR), and two optimization algorithms - genetic algorithm (GA) and particle swarm optimization (PSO) - were applied to construct composition-activity relationship (CAR) models for the Fuzi-Gancao extract. Based on the optimal CAR model, the MIV was introduced to evaluate the contribution of each individual component to the anticancer efficacy of the extract. Results indicated that the SVR-PSO model best depicted the complex relationship between the chemical composition and the inhibition effect of a Fuzi-Gancao extract. The 30 common components were ranked by their absolute MIVs, and the top 8, which corresponded to peaks 17, 25, 22, 13, 23, 28, 5, and 7 in the chromatogram, were tentatively deemed to be the main antitumor components. The integrated strategy shows a novel and efficient approach to understanding the potential contributions of components from complicated herbal medicines, and the identified results suggest certain directions for screening and research into new antitumor drugs.
本研究旨在运用化学计量学和平均影响值（MIV）方法，从附子（中药名）与甘草（中药名）的混合提取物中识别出具有活性的抗肿瘤成分。首先，分别采用高效液相色谱 - 紫外检测法（HPLC - UV）和超高效液相色谱 - 四极杆 - 飞行时间串联质谱法（UPLC - Q - TOF）对31个不同样品中的30种常见成分进行定量和定性分析。同时，采用MTT（3 -（4,5 - 二甲基噻唑 - 2 - 基）- 2,5 - 二苯基四氮唑溴盐）法检测31个不同样品对人宫颈癌HeLa细胞的抑制活性。然后，应用反向传播（BP）神经网络、支持向量回归（SVR）以及两种优化算法——遗传算法（GA）和粒子群优化算法（PSO），构建附子 - 甘草提取物的成分 - 活性关系（CAR）模型。基于最优的CAR模型，引入MIV来评估每种成分对提取物抗癌功效的贡献。结果表明，SVR - PSO模型能最佳地描述附子 - 甘草提取物的化学成分与抑制效果之间的复杂关系。根据绝对MIV值对30种常见成分进行排序，暂定色谱图中对应峰17、25、22、13、23、28、5和7的前8种成分是主要抗肿瘤成分。该综合策略为理解复杂草药中成分的潜在贡献提供了一种新颖且高效的方法，鉴定结果为新抗肿瘤药物的筛选和研究指明了一定方向。
