基于矢量药理网络的中药寒热属性分类模型构建及药性混淆品种寒热属性厘清

There is an urgent demand to build a cold/heat property classification model to facilitate the property theory of Chinese herbal medicine, as well as to clarify the controversial properties of some herbs. Based on the previous NSFC project (No. 30902003), we have established a cold/heat property classification model based on chemical structure profile, besides reviewing other previous studies worldwide. It was concluded that bio-effect should be the key approach to classify the cold and heat properties of herbal medicines. In this study, metabonomics and proteomics techniques will be used to test the serum of SD rats before and after treatment with 5 herbs with typical cold property and another 5 herbs with typical heat property. The omics data will then be collected and analyzed with bioinfomatics approach. Vectorial pharmacological networks of both cold and heat herbs will be built up based on these data, which will contain qualitative and quantitative information of the herbal bio-effect. The vectorial networks will be analyzed using topological structure, key nodes identification, and central subnetwork extraction. Characteristics of pharmacological networks of cold and heat herbs will be identified. Then pattern recognition approaches will be adopted, including template matching algorithm, K near algorithms, and stochastic subspace preferred strategy, to build the cold/heat property classification model based on the bio-effect parameters extracted from network analysis. Four herbs of controversial properties will be selected as target herbs to treat SD rats with the same methods mentioned above. The vectorial pharmacological networks of these herbs will be built up and analyzed. The cold/heat property of these target herbs will be determined with this classification model. This study will provide a scientific model for determination the cold/heat property of herbal medicines, and a new strategy for expanding the Chinese herbal medicine resources as well.

建立科学可靠的药物寒热属性分类方法是药性理论发展的需求，更可为厘清药性混淆品种的寒热属性提供有效方法。项目组前期自然基金项目(30902003)建立了依据药物化学结构谱的中药寒热属性分类模型，并分析了其他药性判别模型，发现从生物效应角度是有效区分中药寒/热属性的最重要方向。本研究拟应用典型寒/热中药干预大鼠前后的代谢组和蛋白质组学实证数据，应用生物信息技术构建含有效应方向与强度信息的中药寒/热性药物的矢量药理网络，利用生物网络分析技术分析网络拓扑结构，识别关键节点、提取中心亚网络信息，对比提取寒、热性药物的药理网络特征；应用模式识别技术，采用模板匹配与K紧邻算法和随机子空间优选策略，构建基于药理网络的中药寒热属性分类模型；选择寒热属性混淆的中药，同样利用干预前后组学数据，构建其矢量药理网络，利用构建的模型厘清其寒热属性，为判定中药寒热属性提供科学方法，也为拓展中药新资源提供策略。

中药寒热属性的科学基础研究一直是中医药学科的关键问题，以客观科学数据定性定量研究中药寒热属性的分类原理是该领域的重要方向。本研究选择典型寒性中药（大黄、黄芩、黄连、黄柏、龙胆草）、热性中药（附子、肉桂、干姜、吴茱萸、仙茅）各5味，以其对SD大鼠进行干预，对比观察给药21天前后各组大鼠的一般状况、脏器系数、行为学、血液生化指标等变化；应用非靶向代谢组学分析技术，全面分析典型寒/热性药物作用于SD大鼠前后的血清代谢物谱，探索与药物寒热属性相关的代谢物。应用网络药理学方法，构建典型寒/热性药物的药理网络，对比典型寒热性中药组的特异性作用靶点和生物学信号通路。结合主成分分析、支持向量机、自组织神经网络等方法，基于所得的寒/热性药物代谢性药理网络数据构建药物寒热属性的分类模型，并应用该模型对于寒热属性不清的四味目标药物的寒热属性进行判定：人参：寒-平；薄荷：寒；姜黄：寒；丹参：平。对于分组有高权重的寒、热性药物干预后变化趋势相同的代谢物为：Myoinositol，L-Valine，L-Leucine，L-Proline，Methylcysteine，L-Arginine，3-Nitrotyrosine，L-Arabinose，L-Arabitol，Ratio of Glucose 6-phosphate/D-Glucose，Ratio of Tryptamine/L-Tryptophan；变化趋势相反的代谢物为：2-Hydroxypyridine，Homocysteine，Ratio of Ornithine/L-Arginine，Ratio of L-Arabinose/L-Arabitol，Tryptamine，Glycolic acid，Maleic acid，Ratio of Oxoglutaric acid/Isocitric acid，Unknown_14。为中药寒热属性的生物效应表征指标体系建设提供方法依据，为拓展中药资源提供寒热药性判断方法，推进药性理论发展。.发表学术论文7 篇，其中SCI 收录3篇、中文核心期刊论文4篇；已接收中文核心论文2篇待发表；在投SCI论文1篇；正在撰写SCI论文1篇。参加美国夏威夷大学学术交流活动3次，就研究设计、检测方法进行讨论，扩大中医药研究的国际影响力；培养博士研究生一名、硕士研究生一名；申请中国发明专利2项。

内容获取失败，请点击重试