Zhang Jie, Guo Wenna, Li Qiao, Sun Faxin, Xu Xiaomeng, Xu Hui
School of Pharmacy, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Yantai University, Yantai, China.
Evid Based Complement Alternat Med. 2020 Mar 9;2020:3141340. doi: 10.1155/2020/3141340. eCollection 2020.
Medicinal property, which is closely related to drug chemical profiling, is the essence of traditional Chinese medicine (TCM) theory and has always been the focus of modern Chinese medicine. Based on dozens of classic and commonly used TCM herbs with recognized medicinal properties, the present study just aimed to investigate the feasibility and reliability of medicinal property discriminant by using H-NMR spectrometry, which provided a mass of spectral data showing holistic chemical profile for multivariate analysis and data mining, including principal component analysis (PCA), Fisher linear discriminant analysis (FLDA), and canonical discriminant analysis (CDA). By using FLDA for two-class recognition, a large majority of test herbs (59/61) were properly discriminated as cold or hot group, and the only two exceptions were Chuanbeimu (Fritillariae Cirrhosae Bulbus) and Rougui (Cinnamomi Cortex), suggesting that medicinal properties interrelate with flavor and body tropism, and all these factors together bring up medicinal property and efficacy. While by performing CDA, 98.4% of the original grouped herbs and 77.0% of the leave-one-out cross-validated grouped cases were correctly classified. The findings demonstrated that discriminant analysis based on holistic chemical profiling data by H-NMR spectrometry may provide a powerful alternative to have a deeper understanding of TCM medicinal property.
药性与药物化学特征密切相关，是中医理论的核心，一直是现代中医研究的重点。基于数十种具有公认药性的经典常用中药，本研究旨在探讨利用氢核磁共振光谱法进行药性判别分析的可行性和可靠性，该方法提供了大量光谱数据，展示了用于多变量分析和数据挖掘的整体化学特征，包括主成分分析（PCA）、费舍尔线性判别分析（FLDA）和典型判别分析（CDA）。通过使用FLDA进行两类识别，绝大多数受试草药（59/61）被正确判别为寒证或热证组，仅有的两个例外是川贝母（川贝母）和肉桂（肉桂皮），这表明药性与五味和归经相关，所有这些因素共同决定了药性和功效。而通过进行CDA，98.4%的原始分组草药和77.0%的留一法交叉验证分组病例被正确分类。研究结果表明，基于氢核磁共振光谱法的整体化学特征数据进行判别分析，可能为深入理解中药药性提供有力的替代方法。
