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EMBARGO: 15th September 2016, 11 AM Pacific / 2 PM Eastern Computational Method Identifies Existing Drugs with Virus-Fighting Potential New screening strategy could speed discovery of antiviral medications A new, computer-based screening method could reveal the virus-fighting potential of drugs originally developed to treat other conditions, reports a study in PLOS Computational Biology. Every year, viral infections cause millions of deaths worldwide. While traditional drug development can yield powerful new antiviral medications, repurposing existing drugs that are already well understood is an appealing alternative. Feixiong Cheng of Vanderbilt University School of Medicine, Tennessee, and colleagues have developed a new strategy to quickly identify drugs with this potential. The researchers used a molecular biology technique called gene-trap insertional mutagenesis to identify hundreds of human genes that enable viruses to hijack a human cell, but are not necessary for the cell itself to survive. A computational framework was then used to screen for existing drugs that already have known effects on these genes. The screening strategy revealed 110 human genes whose protein products could potentially serve as antiviral targets for specific existing drugs. These include several genes involved in HIV1 or Ebola infection. The researchers identified the anti-arrhythmia drug ajmaline as one potential Ebola treatment. Lab experiments and clinical trials are needed to validate the antiviral properties of any promising drugs identified using the new method. Nonetheless, says lead author Feixiong Cheng, it could enable faster discovery of medications for emerging public health threats with no known treatments, including Ebola. In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://dx.plos.org/10.1371/journal.pcbi.1005074 Press-only preview: (to be completed by PLOS) Contact: Name: Donald H. Rubin Email: [email protected] Citation: Cheng F, Murray JL, Zhao J, Sheng J, Zhao Z, Rubin DH (2016) Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by GeneTrap Insertional Mutagenesis. PLoS Comput Biol 12(9): e1005074. doi:10.1371/ journal.pcbi.1005074 Image Caption: Computational Method Identifies Existing Drugs with VirusFighting Potential Image Credit: Isabel Odriozola / Flickr Image Link: (to be completed by PLOS) Funding: This work was supported by the National Natural Science Foundation of China (81573020). This work was also partially supported by National Institutes of Health (NIH) grants (R01LM011177) to ZZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: JLM was previously employed by Zirus, Inc and is currently employed by GeneTAG Technology, Inc. The authors confirm they have no competing interests. Back to the Top About PLOS Computational Biology PLOS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. For more information follow @PLOSCompBiol on Twitter or contact [email protected]. Media and Copyright Information For information about PLOS Computational Biology relevant to journalists, bloggers and press officers, including details of our press release process and embargo policy, visit http://journals.plos.org/ploscompbiol/s/press-and-media . PLOS Journals publish under a Creative Commons Attribution License, which permits free reuse of all materials published with the article, so long as the work is cited. About the Public Library of Science The Public Library of Science (PLOS) PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.plos.org. Disclaimer This press release refers to upcoming articles in PLOS Computational Biology. The releases have been provided by the article authors and/or journal staff. Any opinions expressed in these are the personal views of the contributors, and do not necessarily represent the views or policies of PLOS. PLOS expressly disclaims any and all warranties and liability in connection with the information found in the release and article and your use of such information. Back to the Top