Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug

BMC Systems Biology

Sunghoon Kim⁴  Kyoohyoung Rho²  Luonan Chen³  Claudiu T Supuran⁵  Alessio Innocenti⁵  Jong-Jun Lee¹  Ji-Tea Kim²  Yeongjun Jang²  Young Sun Oh¹  Dae Gyu Kim¹  Ji-Hyun Lee⁶  Taejeong Bae⁶  Hee Sook Lee⁶ 

[1] Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul, Korea;Information Center for Bio-pharmacological Network, Seoul National University, Suwon, Korea;Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200233, China;World Class University Program Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, 151-742, Korea;Dipartimento di Chimica Laboratorio di Chimica Bioinorganica, University of Florence, Via della Lastruccia, 3, Rm. 188 Polo Scientifico, Sesto Fiorentino (Firenze), 50019, Italy;Medicinal Bioconvergence Research Center, Advanced Institutes of Convergence Technology, Suwon, 443-270, Korea

关键词: Shared Neighborhood Scoring (SNS) algorithm;    Drug repositioning;    Tripartite network;   

Others  :  1143861
DOI  :  10.1186/1752-0509-6-80

 received in 2012-01-05, accepted in 2012-05-31,  发布年份 2012

【 摘 要 】

Background

The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning.

Results

In this study, we have established a database we call “PharmDB” which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death.

Conclusions

By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/ webcite, http://biomart.i-pharm.org/ webcite).

【 授权许可】

   
2012 Lee et al.; licensee BioMed Central Ltd.

【 图 表 】

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