ArticleThe Journal of antimicrobial chemotherapy2017
WGS to predict antibiotic MICs for Neisseria gonorrhoeae.
Article in The Journal of antimicrobial chemotherapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 1 of them a synthesis that pooled it.
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Who cites it
105 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
- Sequence analyses to accelerate the development of rapid resistance assays targeting the Neisseria gonorrhoeae gyrA gene.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.npj antimicrobials and resistance · 2026Article
- Mind the governance gap: a one health scoping review of national AMR performance indicators.npj antimicrobials and resistance · 2026Article
- Quantifying the real-world impact of antibiotic use and genetic determinants of resistance on gonococcal dynamics.Nature microbiology · 2026Article
- Vaginal Microbiota Dysbiosis and Infections: A Comprehensive Review of Microbial and Therapeutic Perspectives.International journal of women's health · 2026Review
- Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.NPJ biofilms and microbiomes · 2025Article
- Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.ACS bio & med chem Au · 2025Article
- Linezolid in the Focus of Antimicrobial Resistance ofInternational journal of molecular sciences · 2025Review
- Tetracycline-resistantJAC-antimicrobial resistance · 2025Review
- Large-scale global molecular epidemiology of antibiotic resistance determinants inMicrobial genomics · 2025Article
- Genomics for antimicrobial resistance-progress and future directions.Antimicrobial agents and chemotherapy · 2025Review
- Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistantAntimicrobial agents and chemotherapy · 2024Article
- Article
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Optimising machine learning prediction of minimum inhibitory concentrations inMicrobial genomics · 2024Article
- PredictingMicroorganisms · 2024Article
- Use of genome sequencing to resolve differences in gradient diffusion and agar dilution antimicrobial susceptibility testing performance ofJournal of clinical microbiology · 2023Article
- Time Required for Nanopore Whole-Genome Sequencing of Neisseria gonorrhoeae for Identification of Phylogenetic Relationships.The Journal of infectious diseases · 2023Article
- Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria.Diagnostics (Basel, Switzerland) · 2023Review
45 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
Abstract
Background: Tracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes. Objectives: We investigate whether WGS and simultaneous analysis of multiple resistance determinants can be used to predict antimicrobial susceptibilities to the level of MICs in N. gonorrhoeae. Methods: WGS was used to identify previously reported potential resistance determinants in 681 N. gonorrhoeae isolates, from England, the USA and Canada, with phenotypes for cefixime, penicillin, azithromycin, ciprofloxacin and tetracycline determined as part of national surveillance programmes. Multivariate linear regression models were used to identify genetic predictors of MIC. Model performance was assessed using leave-one-out cross-validation. Results: Overall 1785/3380 (53%) MIC values were predicted to the nearest doubling dilution and 3147 (93%) within ±1 doubling dilution and 3314 (98%) within ±2 doubling dilutions. MIC prediction performance was similar across the five antimicrobials tested. Prediction models included the majority of previously reported resistance determinants. Applying EUCAST breakpoints to MIC predictions, the overall very major error (VME; phenotypically resistant, WGS-prediction susceptible) rate was 21/1577 (1.3%, 95% CI 0.8%-2.0%) and the major error (ME; phenotypically susceptible, WGS-prediction resistant) rate was 20/1186 (1.7%, 1.0%-2.6%). VME rates met regulatory thresholds for all antimicrobials except cefixime and ME rates for all antimicrobials except tetracycline. Country of testing was a strongly significant predictor of MIC for all five antimicrobials. Conclusions: We demonstrate a WGS-based MIC prediction approach that allows reliable MIC prediction for five gonorrhoea antimicrobials. Our approach should allow reasonably precise prediction of MICs for a range of bacterial species.
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