Biologist Miriam Barlow, PhD, of UC Merced, and mathematician Kristina Crona, PhD, of American University, combined laboratory work with mathematics and computer technology to identify optimal cycling strategies that returned bacteria to a pre-resistant state.
Dr. Barlow and Dr. Crona created bacteria in a lab, exposed them to 15 different antibiotics and measured their growth rates. The researchers then computed the probability of mutations to return the bacteria to its harmless state using a software name “Time Machine.”
They tested up to six drugs in rotation at a time and found optimal plans for reversing the evolution of drug-resistant bacteria.
“This shows antibiotics cycling works. As a medical application, physicians can take a more strategic approach,” said Dr. Crona. “Uncovering optimal plans in antibiotics cycling presents a mathematical challenge. Mathematicians will need to create algorithms that can decipher optimal plans for a greater amount of antibiotics and bacteria.”
Going forward, both researchers hope to test the treatment paths in a clinical setting, working with physicians to rotate antibiotics and maximize their efficacy.
More articles on antibiotic resistance:
3 thoughts from former Sen. Bill Frist on combating antibiotic resistance
WHO: Most countries are unprepared to fight antibiotic-resistant bugs
Research reveals time frame during which bacteria is most vulnerable to antibiotics
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.
