Exploiting Drug-disease relationships for computational drug repositioning

Stanford University, Stanford, CA, USA.
Briefings in Bioinformatics (Impact Factor: 9.62). 06/2011; 12(4):303-11. DOI: 10.1093/bib/bbr013
Source: PubMed


Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.

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    • "The average probability of success of total numbers of R&D projects in the cardiovascular system is only 4.86%. Drug repositioning, new use of old drugs, can shorten the development time and provide solutions for the high cost and declined number of new successful drugs of the pharmaceutical companies (Dudley et al., 2011). Computational repositioning strategies can predict new therapeutic indications for FDA-approved drugs, which then have to undergo clinical trials for the new indication (Belch et al., 2003; Ostchega et al., 2007; Shameer et al., 2015). "
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    ABSTRACT: Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD.
    Frontiers in Pharmacology 08/2015; 6. DOI:10.3389/fphar.2015.00179 · 3.80 Impact Factor
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    • "Although repositioning existing drugs for alternative indications is not new, it is only recently that large-scale computational methods are being developed and used (Shaughnessy, 2011). Computational drug repositioning has become a new frontier (Dudley et al., 2011; Hurle et al., 2013; Lu et al., 2013) in today's drug discovery research. Recent methods focus on systematically exploring novel drug-disease therapeutic relationships from large-scale molecular data, such as transcriptomics, genome-wide association study (GWAS), and target screening data. "

    Data Science Journal 05/2015; 14. DOI:10.5334/dsj-2015-009
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    • "Drug repurposing or drug repositioning, which aims to find new therapeutic indications for approved drugs and experimental drugs that fail approval in their initial indication, has offered several advantages over traditional drug development including rescuing stalled pharmaceutical projects, finding therapies for neglected diseases and reducing the time, cost and risk of drug development [1,2]. During the past decade, a number of computational strategies for drug repurposing have been developed [1], including strategies based on the chemical similarity of drugs [3], similarity of drug side effects [4], molecular activity similarity [5], and shared molecular pathology [6]. Among these strategies, the method based on similarity of molecular activity generated from global gene expression profiling now emerges as a promising approach for drug repurposing [5]. "
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    ABSTRACT: Background Drug-induced gene expression dataset (for example Connectivity Map, CMap) represent a valuable resource for drug-repurposing, a class of methods for identifying novel indications for approved drugs. Recently, CMap-based methods have successfully applied to identifying drugs for a number of diseases. However, currently few gene expression based methods are available for the repurposing of combined drugs. Increasing evidence has shown that the combination of drugs may valid for novel indications. Method Here, for this purpose, we presented a simple CMap-based scoring system to predict novel indications for the combination of two drugs. We then confirmed the effectiveness of the predicted drug combination in an animal model of type 2 diabetes. Results We applied the presented scoring system to type 2 diabetes and identified a candidate combination of two drugs, Trolox C and Cytisine. Finally, we confirmed that the predicted combined drugs are effective for the treatment of type 2 diabetes. Conclusion The presented scoring system represents one novel method for drug repurposing, which would provide helps for greatly extended the space of drugs.
    Journal of Translational Medicine 05/2014; 12(1):153. DOI:10.1186/1479-5876-12-153 · 3.93 Impact Factor
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