Evolutionary dynamics of multidrug antibiotic therapy in the model organism Pseudomonas aeruginosa

Gedon, Ashley ORCID

The emergence and dissemination of antibiotic resistance among microorganisms poses a dramatic threat to modern medicine. With the increasing presence of multi-drug resistant bacterial infections, medical doctors are running out of effective drugs quicker than scientists are able to produce them. The lack of effective drugs threatens the functioning of modern medicine, as we are in danger of entering into a “post-antibiotic era” in which doctors are not able to treat even the simplest infections, and complex medicine including cancer treatment, surgery, and transplants is no longer a possibility. At the root of this problem is the enormous ability of bacteria to quickly and easily adapt to virtually any encountered toxin. Understanding the mechanisms behind bacterial evolution in response to various selective pressures is thus essential to combatting the antibiotic resistance crisis. Using evolutionary principles to optimize the deployment of currently effective antibiotics via rational treatment design has been used as a tool to select against antibiotic resistance. Administering antibiotics cyclically or in combination are two alternative regimens that have been suggested to have the potential to slow down or to decrease the selective advantage of resistance evolution in specific circumstances. However, these specific circumstances are not yet agreed upon, and the nature of the reduction in resistance seen with these therapies needs further experimentation. In this study, I aimed to optimize cycling antibiotic therapy with the Gram-negative human pathogen Pseudomonas aeruginosa. Using in vitro evolution experiments, the effects of cycling antibiotics, drug combinations, and a new regimen, termed cycling-in-combinations, on the evolution of drug resistance, were investigated. I found that cycling drugs in combination generally produced the highest extinction rates, the highest bacterial inhibition throughout evolution, and no significantly higher fitness costs when compared to cycling monotherapies and combination regimens. I additionally found that cycling-in-combination therapies showed minimal multidrug resistance in comparison to the respective cycling and combination therapies. Taken together, these results indicate that cycling drugs in combination could be a successful alternative antibiotic therapy that both eradicates large numbers of bacteria, but importantly, also limits bacterial resistance evolution.

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Gedon, Ashley: Evolutionary dynamics of multidrug antibiotic therapy in the model organism Pseudomonas aeruginosa. 2020.

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