UCLA

Abstract Antimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue 1. Resistance to ceftriaxone, the mainstay of gonorrhea treatment, is increasing2,3. Many genotypic mutations are associated with decreased susceptibility to ceftriaxone 4,5. In this study, N. gonorrhoeae genomes from the PathogenWatch database were downloaded and used to train and test different machine learning (ML) models to predict ceftriaxone susceptibility/decreased susceptibility (S/DS). We evaluated seven different ML algorithms with 97 SNPs that are known to be associated with ceftriaxone resistance. After identifying the ML algorithm with the highest performance metrics, the impact score of individual SNPs were calculated. The algorithm was then retrained using various combinations of top scoring SNPs to measure performance. The study identified 5 SNPs that performed well to predict decreased susceptibility and might be promising targets for molecular assays to predict S/DS to ceftriaxone. The ML methods reported here might have applications for predicting AMR within other pathogens.