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      Exploiting large-scale drug-protein interaction information for computational drug repurposing

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          Abstract

          Background

          Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated.

          Results

          We developed a computational method termed “drug-protein interaction-based repurposing” (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect.

          Conclusions

          Detailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).

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          Most cited references50

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          How to improve R&D productivity: the pharmaceutical industry's grand challenge.

          The pharmaceutical industry is under growing pressure from a range of environmental issues, including major losses of revenue owing to patent expirations, increasingly cost-constrained healthcare systems and more demanding regulatory requirements. In our view, the key to tackling the challenges such issues pose to both the future viability of the pharmaceutical industry and advances in healthcare is to substantially increase the number and quality of innovative, cost-effective new medicines, without incurring unsustainable R&D costs. However, it is widely acknowledged that trends in industry R&D productivity have been moving in the opposite direction for a number of years. Here, we present a detailed analysis based on comprehensive, recent, industry-wide data to identify the relative contributions of each of the steps in the drug discovery and development process to overall R&D productivity. We then propose specific strategies that could have the most substantial impact in improving R&D productivity.
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            Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells.

            Lenalidomide is a drug with clinical efficacy in multiple myeloma and other B cell neoplasms, but its mechanism of action is unknown. Using quantitative proteomics, we found that lenalidomide causes selective ubiquitination and degradation of two lymphoid transcription factors, IKZF1 and IKZF3, by the CRBN-CRL4 ubiquitin ligase. IKZF1 and IKZF3 are essential transcription factors in multiple myeloma. A single amino acid substitution of IKZF3 conferred resistance to lenalidomide-induced degradation and rescued lenalidomide-induced inhibition of cell growth. Similarly, we found that lenalidomide-induced interleukin-2 production in T cells is due to depletion of IKZF1 and IKZF3. These findings reveal a previously unknown mechanism of action for a therapeutic agent: alteration of the activity of an E3 ubiquitin ligase, leading to selective degradation of specific targets.
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              The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins.

              Thalidomide-like drugs such as lenalidomide are clinically important treatments for multiple myeloma and show promise for other B cell malignancies. The biochemical mechanisms underlying their antitumor activity are unknown. Thalidomide was recently shown to bind to, and inhibit, the cereblon ubiquitin ligase. Cereblon loss in zebrafish causes fin defects reminiscent of the limb defects seen in children exposed to thalidomide in utero. Here we show that lenalidomide-bound cereblon acquires the ability to target for proteasomal degradation two specific B cell transcription factors, Ikaros family zinc finger proteins 1 and 3 (IKZF1 and IKZF3). Analysis of myeloma cell lines revealed that loss of IKZF1 and IKZF3 is both necessary and sufficient for lenalidomide's therapeutic effect, suggesting that the antitumor and teratogenic activities of thalidomide-like drugs are dissociable.
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                Author and article information

                Contributors
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2014
                20 June 2014
                : 15
                : 210
                Affiliations
                [1 ]Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
                Article
                1471-2105-15-210
                10.1186/1471-2105-15-210
                4079911
                24950817
                2012334c-6bdf-497f-be33-4dec40ed7761
                Copyright © 2014 Liu et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 January 2014
                : 9 June 2014
                Categories
                Methodology Article

                Bioinformatics & Computational biology
                drug repurposing,bayes theorem,drug-protein interaction,mechanism of drug action

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