Antimicrobial resistance (AMR) is the ability of microorganisms to resist
antimicrobial treatments, especially antibiotics. Infections due to antibiotic-resistant bacteria are a threat to modern health care and are responsible for an estimated 700,000 and 33,000 deaths/year globally and in the EU. A major challenge for clinicians managing infection is to precisely identify those patients who should receive antibiotics and those who should not. Current culture-based
methods used to detect and identify agents of infection are inadequate.
the time interval from the collection of patient samples at the ward until the
information is available on antibiotic susceptibility pattern is in the best case
2-4 days in the clinical routine.
Taking advantage of the radical developments in whole genome sequencing
(WGS), bioinformatics, and machine learning (ML) we plan to develop a method for the early detection of bacterial infection, its resistance profile, and drug susceptibility. Just like a radar detects and identifies potential threats early, our proposed system would be able to rapidly detect infectious bacteria, its antimicrobial resistance profile and antibiotic susceptibility, early for preventative or remedial action. Leading to better patient outcomes. In this project, we will cover the most wide-spread infections caused by the pathogens in the WHO
antibiotic-resistant "priority pathogens" list. The approach will be validated
using well-characterized clinical isolates and the proof-of-concept will be
evaluated in a clinical setting.
The method will be designed for use by doctors and other health care
professionals, providing the information needed in order to choose the best
treatment.
