A research team led by scientists at UC San Francisco has developed a computational method to systematically probe massive amounts of open-access data to discover new ways to use drugs, including some that have already been approved for other uses.
The method enables scientists to bypass the usual experiments in biological specimens and to instead do computational analyses, using open-access data to match FDA-approved drugs and other existing compounds to the molecular fingerprints of diseases like cancer. The specificity of the links between these drugs and the diseases they are predicted to be able to treat holds the potential to target drugs in ways that minimize side effects, overcome resistance and reveal more clearly how both the drugs and the diseases are working.
“This points toward a day when doctors may treat their patients with drugs that have been individually tailored to the idiosyncracies of their own disease,” said first author Bin Chen, assistant professor with the Institute for Computational Health Sciences (ICHS) and the Department of Pediatrics at UCSF.
In a paper published online on July 12, 2017, in Nature Communications, the UCSF team used the method to identify four drugs with cancer-fighting potential, demonstrating that one of them—an FDA-approved drug called pyrvinium pamoate, which is used to treat pinworms—could shrink hepatocellular carcinoma, a type of liver cancer, in mice. This cancer, which is associated with underlying liver disease and cirrhosis, is the second-largest cause of cancer deaths around the world—with a very high incidence in China—yet it has no effective treatment.
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