ArticleJAC-antimicrobial resistance2021
Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data.
Article in JAC-antimicrobial resistance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Evolutionary drivers of divergent collateral sensitivity responses during antibiotic therapy.Nature ecology & evolution · 2026Review
- Emerging resistance to novel β-lactam β-lactamase inhibitor combinations in Klebsiella pneumoniae bearing KPC variants.Journal of global antimicrobial resistance · 2025Article
- Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments.PLoS biology · 2025Article
- Navigating collateral sensitivity: insights into the mechanisms and applications of antibiotic resistance trade-offs.Frontiers in microbiology · 2024Review
- Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source.Communications medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCollateral 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.
objectivesTo develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data.
methodsWe 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
resultsWe 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.
conclusionsOur 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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.