UVA’s computer models target antibiotics to combat resistance

With antibiotic resistance on the rise, researchers at the University of Virginia School of Medicine have developed innovative computer models that could give antibiotics the precision to target only specific bacteria in specific parts of the body. This approach seeks to address the rising challenge of antibiotic resistance, which jeopardises one of modern medicine’s most essential weapons in combating infectious diseases.

Antibiotics have long been used to kill bacteria, often damaging both harmful and beneficial bacteria. Over time, this widespread use has contributed to the rise of resistant bacteria, rendering some antibiotics ineffective against certain infections. UVA’s new strategy promises to change this by offering a more targeted approach to treating bacterial infections, potentially reducing the likelihood of bacteria becoming resistant.

The core of UVA’s new approach lies in the development of sophisticated computer models that focus on bacteria’s molecular networks. These models allow researchers to map out how bacteria function, especially in specific environments within the body, such as the stomach or intestines. By targeting bacteria with greater precision, doctors could avoid unnecessary exposure to antibiotics, thereby reducing the risk of resistance and promoting more personalised treatments for patients.

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

The journey to more targeted antibiotics

Papin, alongside PhD student Emma Glass and collaborators, worked tirelessly to develop these computer models. They collaborated with Dr Andrew Warren, from UVA’s Biocomplexity Institute, and together, they mapped out the genetic information of every human bacterial pathogen with available genetic data. This effort provided valuable insight into how bacteria from different parts of the body share certain metabolic properties that could be used to design targeted antibiotics.

Glass, who conducted much of the analysis, said, “Using our computer models we found that the bacteria living in the stomach had unique properties. These properties can be used to guide design of targeted antibiotics, which could hopefully one day slow the emergence of resistant infections.”

By pinpointing shared traits among bacteria in various body regions, the research team uncovered an important vulnerability in harmful bacteria. The hope is that, with further development, these insights will allow doctors to target specific bacteria in specific body areas, thereby reducing the need for broad-spectrum antibiotics and limiting their potential to cause harm.

Promising early results

The team has already successfully tested their computer model approach by inhibiting the growth of harmful stomach bacteria in laboratory experiments. This early success offers a glimpse of the future potential of their approach, though further research is necessary to test its effectiveness against other bacteria and infections.

Papin noted, “We still have much to do to test these ideas for other bacteria and types of infections, but this work shows the incredible promise of data science and computer modelling for tackling some of the most important problems in biomedical research.”

The research team has published their findings in PLOS Biology.