Computational methods for drug repositioning

Abstract

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, I 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. And the Shared Neighborhood Scoring (SNS) algorithm was designed to explore the inferred linkages of known drugs to diseases of interest. I also developed a combinatorial drug discovery system called CDA (Combinatorial Drug Assembler). CDA performs gene expression pattern matching of signaling pathway components to discover synergistic combinatorial drug pairs. Despite successes of several drugs targeting highly specific single disease-associated gene products, many of other drugs show inefficient effect or severe side effects. To overcome these limitations of single protein targeting drug, CDA was developed. CDA provides a new way for rational combinatorial drug discovery. 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/, http://biomart.i-pharm.org/). CDA is freely available at http://cda.i-pharm.org.