Antibiotic combination efficacy (ACE) networks for a Pseudomonas aeruginosa model

Abstract

The spread of antibiotic resistance is always a consequence of evolutionary processes. The consideration of evolution is thus key to the development of sustainable therapy. Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions. We systematically assessed these factors by performing over 1,600 evolution experiments with the opportunistic nosocomial pathogen Pseudomonas aeruginosa in single- and multidrug environments. Based on the growth dynamics during these experiments, we reconstructed antibiotic combination efficacy (ACE) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time. Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that (i) synergistic drug interactions increased the likelihood of bacterial population extinction—irrespective of whether combinations were compared at the same level of inhibition or not—while (ii) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates. In sum, our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores. Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution.

Author summary

Bacterial infections are commonly treated with a combination of antibiotic drugs. However, not all combinations are equally effective, and success is variable. One reason for this variation is that we usually do not know to what extent bacteria are able to adapt to different types of drug combinations. If they can and do adapt, then antibiotic resistance can spread, potentially aggravating the current antibiotic crisis. In the current study, we therefore asked whether combination therapy can be improved by considering the evolutionary potential of the bacteria. To address this question, we systematically assessed the efficacy of antibiotic combinations using controlled laboratory evolution experiments with the opportunistic human pathogen Pseudomonas aeruginosa as a model. We found that 2 factors consistently increase treatment efficacy. First, synergism between the combined drugs (i.e., the 2 drugs enhance each other’s effects) increases the rate of bacterial population extinction and thus clearance rate. Second, evolved trade-offs such as collateral sensitivity (i.e., evolution of resistance to one drug increases susceptibility to the other drug) limit the ability of bacteria to adapt to the antibiotic pair. Our findings may help to optimize combination therapy by focusing on drug pairs that interact synergistically and also lead to evolved collateral sensitivities.