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This article is part of the supplement: Selected articles from the Third International Symposium on Optimization and Systems Biology

Open Access Proceedings

A systems biology approach to identify effective cocktail drugs

Zikai Wu123, Xing-Ming Zhao1* and Luonan Chen45*

Author Affiliations

1 Institute of Systems Biology, Shanghai University, Shanghai, China

2 Business School, University of Shanghai for Science and Technology, Shanghai, China

3 School of Communication and Information Engineering, Shanghai University, Shanghai, China

4 Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

5 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

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BMC Systems Biology 2010, 4(Suppl 2):S7  doi:10.1186/1752-0509-4-S2-S7

Published: 13 September 2010

Abstract

Background

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

Results

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

Conclusions

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.