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

• Published in: JAC-antimicrobial resistance 2021-12, Vol.3 (4), p.dlab175
• Main Authors: Zwep, Laura B, Haakman, Yob, Duisters, Kevin L W, Meulman, Jacqueline J, Liakopoulos, Apostolos, van Hasselt, J G Coen
• Format: Article
• Language: English
• Subjects: Original
• ISSN: 2632-1823; 2632-1823
• DOI: 10.1093/jacamr/dlab175

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.