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      Traditional Chinese medicine network pharmacology: theory, methodology and application.

      1 ,
      Chinese journal of natural medicines
      Elsevier BV

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          Abstract

          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 prioritize disease-associated genes, to predict the target profiles and pharmacological actions of herbal compounds, to reveal drug-gene-disease co-module associations, 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.

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          Author and article information

          Journal
          Chin J Nat Med
          Chinese journal of natural medicines
          Elsevier BV
          1875-5364
          1875-5364
          Mar 2013
          : 11
          : 2
          Affiliations
          [1 ] Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China. shaoli@mail.tsinghua.edu.cn
          Article
          S1875-5364(13)60037-0
          10.1016/S1875-5364(13)60037-0
          23787177
          a70aad1f-5372-455e-8251-834cbef6568e
          Copyright © 2013 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.
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