基于生物药剂学可视化人工神经网络模式的“甘味”中药(甘草)“能缓能和”配伍作用的分子机制研究

以“五味”为代表的中药气味理论是中药药性理论的核心内容之一，阐述传统甘味药物“能缓能和”效应药性的科学内涵及其对方剂配伍的作用是中医药理论研究的关键问题。本研究以中药甘草为模型药物，以无标度赋权网络模式筛选经验方中“缓”“和”作用系列药对；利用生物药剂学研究技术手段，系统考察药对配伍后，主要生物效应成分体内代谢（基于药物体内处置的生物药剂分类学BDDCS）、体外吸收（生物药剂分类学BCS）、及其他物理化学成药性（稳定性、溶出度等）变化规律和趋势；采用人工神经网络结合数据可视化分析的方法对结果进行化学模式识别和图形化表征；综合评价甘草在配伍过程中“能缓能和”的作用特点，明确其化学本质和分子作用机制。本研究拟建立一种基于生物药剂学特征可视化复杂网络模式的甘味中药发挥调和配伍作用的研究方法，以期为中药药性理论研究提供新的思路和实用方法。

The five flavors was the representative and classical nature of Traditional Chinese Medicine herb. To elucidate to the effectiveness and property of the flavors theory is one of the crucial scientific topic in the TCM modernization research. The couplet medicines of Glycyrrhizae Radix et Rhizoma with the clinical effect of mitigation and reconciliation were selected by adopting scale-free weighted network. Our study is based on the in-vivo metabolism analysis by Biopharmaceutics Drug Disposition Classification System (BDDCS), in-vitro absorption study by Biopharmaceutics Classification System (BCS), and preliminary experiments examined the drug ability by testing the stability and dissolution and et al. Artificial neural network combined with data visualization analysis is used to identify the results of the chemical property and molecular mechanisms of the action. A research strategy based on the complex network of the visualized biopharmaceutical characteristics will be established to provide scientific foundation of the study of medicinal properties in Traditional Chinese Medicine theory.