Drug combinations targeting antibiotic resistance

Abstract

While the rise of antibiotic resistance poses a global health challenge, the development of new antibiotics has slowed down over the past decades. This turned the attention of researchers towards the rational design of drug combination therapies to combat antibiotic resistance. In this review we discuss how drug combinations can exploit the deleterious pleiotropic effects of antibiotic resistance and conclude that each drug interaction has its prospective therapeutic application.

Fig. 1. Collateral sensitivity measured with growth inhibition and killing efficacy may not overlap.

This schematic figure represents the growth inhibition-based standard broth dilution assay for defining the minimal inhibitory concentration (left part) of an antibiotic resistant (orange) and sensitive (grey) bacterial strain and their survival rate (right part). Bacterial survival is measured at a high, clinically relevant dose of the antibiotics after a predetermined amount of treatment time. In the presence of the green antibiotic, the effect on the resistant strain’s growth is not different compared to its sensitive counterpart (marked by the decrease of intensity in the orange and grey circles, respectively). However, the similar results in growth inhibition still leave two possibilities open: collateral sensitivity, when the resistant population (orange) is cleared orders of magnitude more efficiently than its sensitive counterpart (grey) or no interaction where both variants are equally affected.

Fig. 2. Limitations and uses of collateral sensitivity.

This hypothetical antibiotic treatment (orange pill) of a heterogeneous population (leftmost flask, represented by different shades of grey bacteria) shows four possible outcomes. A During treatment the initially antibiotic sensitive bacterial population (grey bacteria) will evolve resistance through different mutations (represented by a range of colours). Hence, when the treatment is switched to the blue antibiotic, only those fractions of the resistant population will be eliminated that had specific resistance mutations causing collateral sensitivity (orange bacteria). B If a collateral sensitivity pattern is robust and conserved, the majority of the heterogeneous population will evolve resistance mutations to the orange antibiotic that cause collateral sensitivity to the blue antibiotic. Hence, the second treatment eliminates the population. C However, if this collaterally sensitive resistance mechanism is transiently induced with a specific drug (white tablet) throughout the whole sensitive population, the resistant population can be eliminated. D A similar principle can apply for rapidly spreading resistance determinants on plasmids available in the environment: Here, antibiotic treatment causes the rapid uptake of this resistance plasmid (circular DNA) – this makes the population robustly hypersusceptible to a second antibiotic, clearing the population with great efficacy.

Fig. 3. Effects of synergy and suppression.

The barplot compares the survival of a bacterial strain resistant to the orange antibiotic (orange) and a strain sensitive to it (grey) under single-antibiotic and combination treatments in cases of different antibiotic interactions. In single-antibiotic treatments, the sensitive strain’s survival is reduced by both antibiotics, at varying rates. In suppressive combination treatments, the sensitive strain is less affected compared to the blue antibiotic’s effects, but the orange resistant strain suffers the unimpeded effects of the blue antibiotic, providing the sensitive bacteria a competitive advantage. In synergistic combination treatments, the sensitive strain’s survival is highly reduced compared to the effects of the single antibiotics, but the resistant strain to the orange antibiotic only suffers the effect of the blue antibiotic, allowing the mono-resistant strain to potentially outcompete the sensitive strain.

Fig. 4. Antibiotic interactions on growth inhibition and clearance efficacy might be different.

The typical checkerboard assay consists of gradients of two antibiotics, which increase in concentration on two different axes. Here, antibiotics blue and orange (blue pill and orange pill), are presented on a checkerboard assay, where antibiotic blue increases in concentration along the x axis, and antibiotic orange increases in concentration on the y axis. As the concentrations increase, bacterial growth is reduced, marked by a decrease in the darkness of the grey circles. This creates a front of inhibition, where bacteria stop growing. The shape of this inhibition front is hyperbolic, as shown by the red dashed line, suggesting a synergistic combination compared to the expected additive effect (grey dashed line). However, inspection of inhibited wells for clearance efficacy after a predetermined amount of treatment time (barplot on the upper right), the survival of bacteria when receiving the maximal dose of the antibiotics and their combinations betrays a suppressive interaction, the exact opposite of the effect expected by growth inhibition data.