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, reducing our overall contact with antibiotic drugs, and thus reducing the chance that the bacteria throughout our bodies could become resistant to them.
“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 antibiotic drugs, 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 today, dangerous bacteria are increasingly developing an antibiotic resistance, sapping these medicines 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 metabolic 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 contact with antibiotics, these targeted treatments could replace non-targeted treatments, 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
Using 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 curb 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
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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.
