Antibiotic resistance continues to be an increasingly worrisome global health problem. Though antibiotics have been foundational in the fight against certain types of pathogens their over/misuse has led to the development of antibiotic-resistant bacteria and pathogens, limiting the usefulness of existing antibiotics. However, there are few incentives to encourage researchers to look for new antibiotic treatments.
Antibiotic resistance may be particularly troublesome for bone infections. As our population ages, alongside a rise in the number of orthopedic procedures needed, there is a higher risk of bone infections. Routine procedures like hip or knee replacements often lead to infections. Unfortunately, existing antibiotics are not always effective at treating these infections, because they operate on a systemic, rather than local, level, contributing to antibiotic resistance. While some localized treatments exist (such as antibiotic-loaded cements), none are designed specifically for bone tissue.
To counter the need for localized antibiotics and the growing prevalence of antibiotic resistant pathogens, researchers at Brigham and Women’s Hospital have turned to artificial intelligence to help pave the way towards new antibiotic candidates, particular for bone infections. Their work is described in Nature Biomedical Engineering.
Researchers started by using polymethylmethacrylate, a type of bone cement matrix consider top-tier by the FDA. Through a pre-clinical model, researchers identified a range of molecules that could be developed for antibiotic use. They also reviewed each molecule for any bacteria that may have resistance to it. The team then developed a model of a tibial injury infected with Staphylococcal, a common type of infection in orthopedic procedures, to test a newly created, antibiotic-laced bone cement.
The team’s efforts paid off with the discovery of an antibiotic (VCD-077), which did not affect the structural integrity of the cement while showing great efficacy against resistant bacteria.
While the team still has hurdles to address with their approach (such as how their model may translate into human subjects), the team notes that their approach using technology to find and design antibiotic molecules and develop localized treatments could help ward of the threat of antibiotic resistance.