Researchers Develop Computer Models for More Targeted Antibiotics - Video

With antibiotic resistance a growing problem, University of Virginia School of Medicine researchers have developed cutting-edge computer models that could give the disease-fighting drugs a laser-like precision to target only specific bacteria in specific parts of the body. The findings are published in PLoS Biology.

The new approach would dramatically limit how often bacteria are exposed to antibiotics, reducing the chance they could become resistant to antibiotics. Further, the approach would represent a significant step forward for precision medicine.

The researchers in Papin’s lab developed sophisticated computer models of every human bacterial pathogen with sufficient genetic information available.

Researchers then analyzed all those models and identified shared traits among the bacteria. This analysis yielded the discovery that bacteria in certain parts of the body, such as the stomach, tended to share metabolic properties. Basically, where they live shapes how they function.

The shared similarities among the microbes in different locales could be the Achilles’ heel for harmful bacteria in our bodies. With further research, doctors may be able to target specific types of bacteria in specific areas, reducing the need for broad-spectrum antibiotics.

Putting their computer-modeling approach to the test, researchers have already found that they could inhibit the growth of harmful stomach bugs in lab experiments.

“Many biomedical challenges are incredibly complex, and computer models are emerging as a powerful tool for tackling such problems,” said researcher Jason Papin, PhD, of UVA’s Department of Biomedical Engineering. “We’re hopeful that these computer models of the molecular networks in bacteria will help us develop new strategies to treat infections.”

Reference: Glass, E. M., Dillard, L. R., Kolling, G. L., Warren, A. S., & Papin, J. A. (2024). Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens. PLoS biology, 22(11), e3002907.