中药网络药理学:理论、方法与应用(英文)

Traditional Chinese medicine network pharmacology: theory,methodology and application

中医学将病人当作一个具有不同状态的系统来进行治疗,具有悠久的历史,积累了大量的方剂。中医学的整体观念与网络药理学、网络生物学等前沿科技的核心思想具有相通之处,也符合了对肿瘤等复杂疾病进行系统性治疗的需求。'中药网络药理学'这一新领域的研究旨在从系统层次和分子水平揭示中药方剂的奥秘,促进中药研究从当前的'单一靶标,单一药物'模式转向'网络靶标,多成分药物'的新模式。近年来,中药网络药理学的一系列方法得以创建,包括基于网络的疾病基因预测、中药成分的靶标谱和药理活性预测、药物-基因-疾病的共模块分析、中药方剂多成分协同作用的大规模筛选、中药方剂的配伍规律和网络调节机理分析等。这些方法在清络饮、六味地黄丸等方剂的药效物质与作用机理等方面得到了有效应用。研究结果表明中药网络药理学能够为中医学从基于经验的医学迈向基于证据的医学提供新的途径,并能加速中药药物发现的进程,同时改进当前的药物研究策略。

Traditional Chinese medicine （TCM） has a long history of viewing an individual or patient as a system with different statuses, and has accumulated numerous herbal formulae. The holistic philosophy of TCM shares much with the key ideas of emerging network pharmacology and network biology, and meets the requirements of overcoming complex diseases, such as cancer, in a systematic manner. To discover TCM from a systems perspective and at the molecular level, a novel TCM network pharmacology approach was established by updating the research paradigm from the current ＂one target, one drug＂ mode to a new ＂network target, multi-components＂ mode. Subsequently, a set of TCM network pharmacology methods were created to priofitize disease-associated genes, to predict the target profiles and pharmacological actions of herbal compounds, to reveal drug-gene-disease co-module associa- tions, to screen synergistic multi-compounds from herbal formulae in a high-throughput manner, and to interpret the combinatorial rules and network regulation effects of herbal formulae. The effectiveness of the network-based methods was demonstrated for the discovery of bioactive compounds and for the elucidation of the mechanisms of action of herbal formulae, such as Qing-Luo-Yin and the Liu-Wei-Di-Huang pill. The studies suggest that the TCM network pharmacology approach provides a new research paradigm for translating TCM from an experience-based medicine to an evidence-based medicine system, which will accelerate TCM drug discovery, and also improve current drug discovery strategies.