NIH researchers unveil test for potential infection therapies | Courtesy of Shutterstock
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Carol Ostrow | Nov 11, 2016

NIH researchers unveil test for potential infection therapies

Scientists from three different branches of the National Institutes of Health (NIH) have crafted a method to isolate certain drug combinations to combat antibiotic-resistant infections by developing a rapid screening assay.

 

Three researchers from the complex’s National Center for Advancing Translational Sciences (NCATS), Clinical Center and National Institute of Allergy and Infectious Diseases (NIAID) pooled their knowledge to advance a way of efficiently screening thousands of drugs and drug combinations for possible solutions to drug-resistant bacteria. Their efforts have made progress toward tackling both hospital-borne infections and emerging infectious diseases.

 

NCATS’ Wei Zheng, Dr. Peter Williamson of NIAID and NIH Clinical Center’s Dr. Karen Frank screened approximately 4,000 approved drugs and compounds, including antibiotics, anti-fungals, antiseptics and an antiviral, antimalarial and anticancer drug compound.

 

First, they identified 25 that curtailed the growth of certain drug-resistant strains of Klebsiella pneumonia, a type known to cause fatalities; then, they sought combinations of those drugs that might work. By matching up their newly identified drugs with standard antibiotics not shown to be effective alone, the researchers discovered four combinations that could suppress the growth of Klebsiella pneumonia, proving that certain antibiotics were strengthened in the presence of a second drug — or even a third, in the case of severe infections.

 

“We wanted to see which three-drug combinations made sense,” Zheng said. “We screened hundreds of drugs against Klebsiella pneumoniae, and tested 15 combinations against the 10 strains. We found three sets of three-drug combinations had the most activity, and think these three combinations eventually may be useful to clinicians.”

 

The scientists hope the discovery can ultimately help clinicians make “real-time” treatment decisions for several highly resistant infections.

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