Antibiotic drug-resistance continues to be one of the toughest global issues that clinicians face today. Development of new antibiotic classes with different mechanisms of action from currently prescribed drugs has been slow. However, evidence continues to surface that supports the premise that antibiotics which have been out of use could still be effective in treating drug-resistant bacteria.

Now, evolutionary biologists from the University of California (UC), Merced teamed up with mathematicians from American University and found a method to return bacteria to a preresistant state through the use of 15 common antibiotics. The implications of their discovery could have a major impact for physicians who employ antibiotic cycling tactics to keep infections at bay.        

The findings from this study were published online today in PLOS ONE through an article entitled “Rational Design of Antibiotic Treatment Plans: A Treatment Strategy for Managing Evolution and Reversing Resistance.”

“Doctors don't take an ordered approach when they rotate antibiotics,” said Miriam Barlow Ph.D., associate professor at UC, Merced and senior author on the current study. “The doctors would benefit from a system of rotation that is proven. Our goal was to find a precise, ordered schedule of antibiotics that doctors could rely on and know that in the end, resistance will be reversed, and an antibiotic will work.”

While antibiotic resistance is a natural result of bacterial evolution, given the selective pressure they are put under by the drugs, the ability to stay out in front of the development of resistance is the key to fighting tough infections. In an attempt to compensate for the bacterial evolution mechanism, many physicians may reduce, rotate, or even discontinue all together various antibiotics, with the hope that they will become effective for short periods in the future.  

In the current study, biologists decided that teaming up with mathematicians in order to take a computational approach for reversing the resistance mechanism was the best tactic for success.   

“We have learned so much about the human genome as well as the sequencing of bacteria,” explained Kristina Crona, Ph.D., assistant professor in the department of mathematics and statistics at American University and co-author on the current study. “Scientists now have lots and lots of data, but they need to make sense of it. Mathematics helps one to draw interpretations, find patterns and give insight into medical applications.”

Specifically, the investigators exposed bacteria in the laboratory to 15 different antibiotic compounds and measured their rate of growth. Consequently, they researchers computed the probability of mutations to return the bacteria to its harmless state

Additionally, Dr. Barlow and her colleagues identified optimized treatment plans based on their observations of the highest probabilities of selecting for reversions of amino acid substitutions and returning drug-resistance genes to the wild type state.

“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 deliver optimal plans for a greater amount of antibiotics and bacteria.”

Dr. Barlow and Dr. Crona are very excited about their findings and optimistic that working with doctors rotate antibiotics systematically and efficiently will increase the efficacy of the compounds.

“This work shows that there is still hope for antibiotics if we use them intelligently,” stated Dr. Barlow. “More research in this area and more research funding would make it possible to explore the options more comprehensively.”

Previous articleHow Histone H3.3 Helps Hobble Jumping Genes
Next articleThe Big Data Addiction—NGS Has It Bad