Diagnosing Infections with Multi-Drug Resistant Microorganisms using AI-powered Genomic Antibiotic Susceptibility Prediction from Long-Read Sequencing Data

The silent pandemic of antimicrobial resistance (AMR) is a growing global health crisis with a projected annual mortality rate of 10 million by 2050.

Addressing this crisis critically depends on fast, accessible and precise diagnostics to improve detection and prevention of infection and guide antibiotic therapy.

Current diagnostics often have insufficient turnaround time or data for timely detection and prevention of outbreaks or precision antibiotic therapy guidance, and accessibility is often limited due to the required infrastructure and expert personnel.

To overcome these shortcomings, and address AMR, we are developing a diagnostic based on accelerated whole genome sequencing linked with AI-assisted data analysis.

This project termed DRAIGON includes a consortium of internationally recognized experts in sequencing platform and assay development, bioinformatics and machine learning, clinical microbiology, infection prevention and control, antibiotic therapy and stewardship, and health economics to jointly develop our in vitro diagnostic solution to target virtually any pathogen-antibiotic combination.

We propose to validate and demonstrate the clinical utility of DRAIGON focusing on bloodstream and prosthetic joint infections at five independent sites, including one located in a medium income country representative of areas with a high AMR burden and reduced access to laboratory infrastructure.

The diagnostic will be easy to implement and functions as an early detection system to prevent the cross-border spread of pathogens.

In rapidly and accurately providing pathogen ID and type, a comprehensive antibiogram, and outbreak cluster information directly from genome data and in a single assay, this innovative high-resolution diagnostic aims to reinforce the global fight against AMR infections and enable the antibiotic stewardship goals of right antibiotic, at the right dose, for the right patient, and at the right time.