Surveillance and AI-Powered Prediction of AMR to Inform Antibiotic Therapy

The burden of antimicrobial resistance (AMR) has been acknowledged worldwide by leading health institutes. Besides the need for new antibiotics, efforts are required in the field of AMR surveillance and diagnostics for effective infection prevention and empirically informed selection of first line treatments. Next-generation sequencing (NGS), has emerged as an alternative to established molecular tests for the detection, identification, typing and functional characterization of AMR pathogens. The advantage of NGS over other tests resides in the technique’s potential to integrate into a single assay i) the sensitive and comprehensive detection of pathogens with ii) the characterization of AMR based on genomic information. Yet, several limitations impair adoption of NGS for AMR testing, including a lack of validated NGS and bioinformatics workflows, as well as slow turnaround times associated with NGS. For complex sample types such as native patient or environmental specimen, common in culture-free testing, high concentrations of background DNA relative to pathogen DNA further diminishes the utility of the technology. To overcome the aforementioned challenges, we have designed a set of isolate-based and culture-free assays for the identification and characterization of AMR pathogens. In this talk, we present our findings from the routine application of these assays in our lab and explore the results from their clinical validation.

 

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