Multidrug evolutionary strategies to reverse antibiotic resistance

Abstract

Antibiotic treatment has two conflicting effects: the desired, immediate effect of inhibiting bacterial growth and the undesired, long-term effect of promoting the evolution of resistance. Although these contrasting outcomes seem inextricably linked, recent work has revealed several ways by which antibiotics can be combined to inhibit bacterial growth while, counterintuitively, selecting against resistant mutants. Decoupling treatment efficacy from the risk of resistance can be achieved by exploiting specific interactions between drugs, and the ways in which resistance mutations to a given drug can modulate these interactions or increase the sensitivity of the bacteria to other compounds. Although their practical application requires much further development and validation, and relies on advances in genomic diagnostics, these discoveries suggest novel paradigms that may restrict or even reverse the evolution of resistance.

Fig. 1. Physiological interactions and cross-resistance

(A) Isoboles of minimum inhibitory concentration (MIC) are shown in the two-drug concentration space for different drug interactions. The MIC of each drug alone occurs where the isobole intersects each drug axis. When the effect of the two drugs is equal to the effect expected when combining two identical drugs, the shape of the MIC line is linear and the drugs are said to be noninteracting (104). Synergistic drugs require less-than-expected concentrations, corresponding to a concave MIC line, whereas antagonistic interactions require higher drug concentrations, producing a convex line. Finally, drug interactions are suppressive when their effect in combination is less than that of one of the drugs alone, appearing as a nonmonotonic isobole. (B) Cross-resistance and collateral sensitivity: A mutation or acquired gene conferring resistance to drug A can also increase resistance (positive cross-resistance) or decrease resistance (negative cross-resistance or collateral sensitivity) to drug B without otherwise changing the shape of the interaction.

Fig. 2. Selection inversion approaches and potential strategies

(A) In typical drug interactions, the region of growth of a single-drug–resistant mutant (e.g., A-resistant, dashed area) completely covers the region of growth of the drug-sensitive wild type (gray area), and the mutant therefore always outcompetes the wild type. (B to D) There are three principal ways for establishing a concentration regime (*) that selects against resistance: (B) When drug A suppresses drug B, the MIC isobole is nonmonotonic, and so scaling it along the A axis because of resistance leaves a selection-inverting regime. (C) An antagonistic interaction can become synergistic with the acquisition of resistance, making the mutant more sensitive to the combination. (D) Collateral sensitivity, when the MIC of drug B decreases as a result of resistance to drug A, allowing selection against resistance even in the absence of A. (E) Using selection inversion approaches on a nonresistant population can decrease the probability of resistance evolution and make long-term therapy more likely to succeed. (F) Selection against resistance can also be used as part of a two-phase strategy against a population with resistant mutants. The drug-resistant mutants are selected out of the population in the first phase, allowing a previously ineffective antibiotic to be used in the second.

Fig. 3. The efficacy and potential failure of cycling collaterally sensitive antibiotics

(A) Fitness landscapes in collaterally sensitive antibiotics. Genotypes that are resistant to drug A or drug B appear as fitness peaks when the environment contains the drug to which they are resistant but as fitness valleys in the other drug treatment. In principle, alternating the drugs can lead to a cycle of evolution switching between these genotypes (solid arrows). However, doubly resistant mutants can evade this trap (dashed arrows). (B) Two possible evolutionary trajectories in the MICs of component drugs in antibiotic cycling. Ideally, resistance will alternate between two states (solid arrows). However, repeated accumulation of resistance mutations can also create double-resistance, even in the case where each individual mutation induces collateral sensitivity (dashed arrow).