Antibiotic Resistance Dynamics in Gonorrhea Management

The global rise of antimicrobial resistance in Neisseria gonorrhoeae poses a critical threat to public health, undermining the efficacy of current first-line antibiotics and increasing the risk of treatment failure and onward transmission. Historically, gonorrhoea has been managed through empiric therapy guided by population‐level resistance surveillance, with treatment recommendations altered when resistance prevalence crosses predefined thresholds. However, the lag between resistance emergence and guideline updates can allow resistant strains to become established. Contemporary approaches integrate enhanced surveillance, molecular diagnostics and mathematical modelling to inform timely adaptation of treatment regimens. Strategies include lowering the prevalence “switch threshold” for introducing new antibiotics, deploying rapid point-of-care tests to detect resistance markers, and personalising therapy using predictive algorithms. In parallel, vaccine development and targeted immunisation campaigns are explored as means to reduce infection prevalence and thus antibiotic pressure. Multidisciplinary efforts are now focusing on optimising the balance between curing individual infections and slowing the population-level spread of resistance, with particular attention to high-risk groups such as men who have sex with men (MSM) and other key populations. Effective management depends on robust surveillance systems, integration of drug-susceptibility testing into routine care, and adaptive guidelines that anticipate shifts in resistance dynamics.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Antibiotic Resistance Dynamics in Gonorrhea Management publication trend

The graph below shows the total number of articles in antibiotic resistance dynamics in gonorrhea management across all publications each year (not limited to Nature Index journals).

Technical terms

Antimicrobial resistance (AMR): The ability of bacteria to survive exposure to antibiotics that would normally kill them or inhibit their growth.

Empiric therapy: Treatment initiated before specific pathogen susceptibility is known, based on expected resistance patterns.

Switch threshold: The resistance‐prevalence level at which guidelines recommend changing the first-line antibiotic.

Drug-susceptibility testing (DST): Laboratory assays that determine whether a bacterial isolate is sensitive or resistant to particular antibiotics.

Machine learning models: Computational algorithms that identify patterns in data to predict outcomes, such as antibiotic susceptibility.

Vaccine efficacy: The reduction in disease incidence among vaccinated individuals under ideal conditions, expressed as a percentage.

References

1. Assessing thresholds of resistance prevalence at which empiric treatment of gonorrhea should change among men who have sex with men in the US: A cost-effectiveness analysis. PLOS Medicine (2024).
2. Personalizing the empiric treatment of gonorrhea using machine learning models. PLOS Digital Health (2024).
3. The potential impact of a vaccine on Neisseria gonorrhoeae prevalence among heterosexuals living in a high prevalence setting. Vaccine (2023).