Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data

Abstract

Background Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking. Objectives To develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data. Methods We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 Escherichia coli strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database. Results We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions. Conclusions Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future.

Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study

“…Bacterial cross-resistance has been studied in experimental [5][6][7][8][9][10][11] and clinical settings [13][14][15][16][17]. However, to the best of our knowledge, cross-resistance patterns have not been compared between clinical sample sources, while controlling for biases arising from the retrospective nature of such data.…”

“…Cross-resistance is the phenomenon where a bacterial isolate that is susceptible (or resistant) to a particular drug, will often be susceptible (or resistant) to a different drug. Cross-resistance of bacteria to different drugs is a commonly observed phenomenon in experimental [5][6][7][8][9][10][11][12] and clinical settings [13][14][15][16][17]. It is especially prevalent between antibiotics from the same antibiotic class, although differences in the drugs may lead to imperfect associations [12].…”

“…As a result, it is perhaps not surprising that discordant resistance is rarely observed in clinical settings [13,14]. Nonetheless, a recent paper proposed a method of analysis of clinical MIC data that is somewhat analogous to the experimental approach, that not only examines the existence of cross or discordant resistance, but attempts to establish direction of effect [14]. They found some of the limited discordant (and cross) resistance present was unidirectional.…”

“…However, such experimental conditions do not necessarily parallel bacteria living in human populations. As a result, it is perhaps not surprising that discordant resistance is rarely observed in clinical settings [13,14]. Nonetheless, a recent paper proposed a method of analysis of clinical MIC data that is somewhat analogous to the experimental approach, that not only examines the existence of cross or discordant resistance, but attempts to establish direction of effect [14].…”

Dynamic collateral sensitivity profiles highlight challenges and opportunities for optimizing antibiotic sequences

“…For example, the molecular mechanisms behind collateral sensitivity are known in relatively few cases [36, 51], and it is unclear the extent to which collateral profiles are conserved across diverse species [52]. In addition, collateral drug pairs are difficult to identify in clinical settings, despite notable recent progress [53, 54], and somewhat surprisingly, little is known about how collateral profiles change under continued selection, with much of the work performed only recently in other organisms such as cancer [37, 55].…”