Computational Method Identifies Existing Drugs With Virus-Fighting Potential

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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.

Existing drugs could have undiscovered antiviral properties. Courtesy of Isabel Odriozola, Flickr
 

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 HIV-1 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.

Reference: Cheng F, Murray JL, Zhao J, Sheng J, Zhao Z, Rubin DH (2016) Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis. PLoS Comput Biol 12(9): e1005074. doi:10.1371/ journal.pcbi.1005074

Source: PLOS

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