Abstract
Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.
Highlights
Developing and discovering a new drug is a very costly and time consuming process, which can take 10–17 years with a cost of 1.3 billion dollars
Traditional drug repositioning methods primarily use information on chemical structure, side effects, and drug phenotypes and explore similar drugs based on the assumption that structurally similar drugs tend to share common indications [2,3,4]
To define a pathway-drug association, pathway-drug enrichment is established from the drug phenotype expression profile (CMap: Connectivity Map) [6, 7], which contains the gene expression profiles obtained from five different cancer cell lines treated with 1309 (v2) small drug molecules, most of which are Food and Drug Administration (FDA)-approved drugs, for a total 6100 data points representing gene expression results with control vehicle samples
Summary
Developing and discovering a new drug is a very costly and time consuming process, which can take 10–17 years with a cost of 1.3 billion dollars. Swamidass [5] used chemical structure data to identify unexpected connections between a known drug and a disease and explored the hypothesis that if a drug has the same target as a known drug, this new drug would have activity against the disease. As another approach, Keiser et al used 3665 US FDA-approved and investigational drugs that together had hundreds of targets, defining each target by its ligands. The chemical similarities between the drugs and ligand sets predicted thousands of unanticipated associations, BioMed Research International which have been used to develop new indications for many drugs
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