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.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

“…These results indicate that drug interactions underlie a natural trade-off between short-term efficacy and long-term evolutionary potential (38). In addition, recent work has shown that cross-resistance (or collateral sensitivity) between drugs in a combination may also significantly modulate resistance evolution (27,39,26). As a whole, these studies show that drug interactions and collateral effects may combine in complex ways to influence evolution of resistance in multi-drug environments.…”

“…In recent years, significant efforts have been devoted to designing evolutionarily sound strategies that balance short-term drug efficacy with the long-term potential to develop resistance. These approaches describe a number of different factors that could modulate resistance evolution, including interactions between bacterial cells (3,4,5,6,7,8), synergy with the immune system (9), spatial heterogeneity (10,11,12,13,14,15), epistasis between resistance mutations (16,17), precise temporal scheduling (18,19,20,21), and statistical correlations between resistance profiles for different drugs (22,23,24,25,26,27,28,29,30,31).…”

Collateral sensitivity interactions between antibiotics depend on local abiotic conditions

“…This raises the possibility that local environmental conditions could influence both the emergence of collateral sensitivity (by affecting which of the possible pathways to resistance are most strongly selected during antibiotic exposure) and its expression (by modifying the phenotypic effects of resistance alleles when bacteria are exposed to a second antibiotic). To date, research on collateral sensitivity interactions has focused on testing many combinations of antibiotics 5,6,9,14 , multiple strains 10 , or many replicate populations for individual antibiotic combinations 11 . Therefore, the role of local abiotic conditions in the emergence and expression of collateral sensitivity interactions remains unclear.…”

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

“…These anti-resistance approaches exploit different features of the population dynamics, including competitive suppression between sensitive and resistance cells (14,15), synergy with the immune system (16), precise timing of growth dynamics or dosing (17,18), responses to subinhibitory drug doses (19), and band-pass response to periodic dosing (10). Resistance-stalling strategies may also exploit spatial heterogeneity (20,21,22,23,24,25), epistasis between resistance mutations (26,27), hospital-level dosing protocols (28,29), and regimens of multiple drugs applied in sequence (28,30,18,31,19,32) or combination (33,34,35,36,37,38,39,40), which may allow one to leverage statistical correlations between resistance profiles for different drugs (41,42,43,44,39,37,45,46,47,48). As a whole, these studies demonstrate the important role of community-level dynamics for understanding and predicting how bacteria respond and adapt to antibiotics.…”