Using Maths to Combat Antibiotic Resistant Bacteria

Imagine a world where even simple cuts can be life threatening. That is the potential of a world without antibiotics. Along with an overuse of antibiotics in farming, careless and sometimes unnecessary prescriptions in clinics have contributed to increasing rise in antibiotic resistant bacteria, threatening public health. Worryingly, few new classes of antibiotics have been discovered recently, prompting the need to find new ways of tackling this problem.

One solution is to use multiple antibiotics to create obstacles in the road to resistance. However, due to the large number of antibiotics available, experimentally determining which antibiotic course has the smallest chance of generating resistant mutants is difficult.

In their recent article in PLoS Computational Biology, researchers at the Moffitt Cancer Center addressed the problem by developing a novel mathematical model to test sequential treatment regimens. Under principal investigator Dr. Jacob G. Scott, scientists computationally modeled the evolution of bacteria exposed to different antibiotics and found the probability that resistance developed. Their results suggested that using the same antibiotics in different orders can not only prevent resistance but also promote it—surprisingly, approximately 70 percent of their tested sequences actually lead to antibiotic resistance! Usefully, though, as all of their tested drugs are already FDA approved, sequences predicted by the model to prevent resistance can be quickly tested in the laboratory and then immediately used in clinical trials.

This research serves as a warning that arbitrary prescription of antibiotics can lead to inadvertent development of resistance, but that a careful selection of antibiotic sequences may be hugely beneficial in preventing it. Using the arsenal of drugs already at our disposal to their best advantage is a key step in fighting against the growth of antibiotic resistance bacteria.