In their battle against antibiotics, bacteria are gaining an edge, becoming more and more resistant to antibiotic attacks. But a new paper published in PLOS Biology suggests that computer models could contribute to making more targeted antibiotics, with a reduced risk of increasing bacteria’s antibiotic resistance.
According to the authors of the paper, these laser-like antibiotics could attack specific bacteria in specific areas of our bodies once created, restricting the bugs’ chances to adapt to the threat of antibiotic medicines.
“Many biomedical challenges are incredibly complex, and computer models are emerging as a powerful tool for tackling such problems,” said Jason Papin, a study author and a professor of biomedical engineering at the University of Virginia, in a press release. “We’re hopeful that these computer models of the molecular networks in bacteria will help us develop new strategies to treat infections.”
Antibiotic Adaptations
When we take antibiotics, we open an opportunity for the bacteria inside our bodies to adapt, developing an antibiotic resistance. Due to the widespread use of antibiotic drugs in modern medicine, dangerous bacteria are increasingly developing this resistance, sapping these drugs of their ability to fend off disease.
To address this issue, a team of researchers made a series of computer models of dangerous bacteria, then analyzed the models, identifying their shared metabolic traits. Their analysis revealed a series of traits that they could use to make customizable antibiotics, allowing them to target particular bacteria in particular body parts, instead of targeting the bacteria throughout the body.
Curbing our overall contact with antibiotics and curtailing the threat of antibiotic resistance, these targeted treatments could replace traditional, non-targeted medicines, which attack a broad spectrum of bacteria.
Read More: Antibiotic-Resistant Bacteria: What They Are and How Scientists Are Combating Them
An Alternative to Broad Bacterial Treatments
Applying a type of computer model called a genome-scale metabolic network reconstruction (GENRE), the researchers discovered that certain metabolic traits are common in certain bacteria, such as stomach bacteria.
“Using our computer models we found that the bacteria living in the stomach had unique properties,” said Emma Glass, a study author and a student of biomedical engineering at the University of Virginia, in the press release. “These properties can be used to guide design of targeted antibiotics, which could hopefully one day slow the emergence of resistant infections.”
Testing this approach in the lab, the study authors showed that targeted antibiotics could stop the survival and spread of stomach bacteria, indicating their potential as a precision-medicine treatment.
“We still have much to do to test these ideas for other bacteria and types of infections,” Papin said, in the press release. “But this work shows the incredible promise of data science and computer modeling for tackling some of the most important problems in biomedical research.”
Read More: COVID-19 and Drug-Resistant Superbugs Are a Frightening Combination
Article Sources:
Our writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:
PLOS Biology. Niche-Specific Metabolic Phenotypes Can Be Used to Identify Antimicrobial Targets in Pathogens
Sam Walters is a journalist covering archaeology, paleontology, ecology, and evolution for Discover, along with an assortment of other topics. Before joining the Discover team as an assistant editor in 2022, Sam studied journalism at Northwestern University in Evanston, Illinois.
