Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug
- Equal contributors
1 Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul, Korea
2 World Class University Program Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, 151-742, Korea
3 Information Center for Bio-pharmacological Network, Seoul National University, Suwon, Korea
4 Dipartimento di Chimica Laboratorio di Chimica Bioinorganica, University of Florence, Via della Lastruccia, 3, Rm. 188 Polo Scientifico, Sesto Fiorentino (Firenze), 50019, Italy
5 Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200233, China
6 Medicinal Bioconvergence Research Center, Advanced Institutes of Convergence Technology, Suwon, 443-270, Korea
BMC Systems Biology 2012, 6:80 doi:10.1186/1752-0509-6-80Published: 2 July 2012
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.
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.
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).