According to the World Health Organization, antimicrobial resistance is an increasingly serious threat to global public health, both clinically and economically, that requires actions across all government sectors and society. Antimicrobial resistance occurs naturally over time but the misuse and overuse of antimicrobials accelerate this process. However, the turnaround time needed to get a diagnosis of infection with the standard method is typically 24 to 48h due to the bacterial culture required prior mass spectrometry analysis. During this time, patients receive broad spectrum antibiotics which may not be needed and/or not efficient and, in all cases, are known to increase the selection of new microbial resistance in the population. Through the collaboration of Thermo Fisher Scientific, Evosep Biosystems and the Laboratory of Pr. Arnaud Droit at the CHU de Québec research center – Université Laval, we aim to develop a new strategy for bacterial identification named MICRO-AI. This method, based on the use of high-throughput and high sensitivity mass spectrometry analyses associated with artificial intelligence, will provide a bacterial identification in biological specimens in less than three hours without the need of a bacterial culture.
Therefore, the patient could be directly treated with specific antibiotics and only if needed, reducing the occurrence of new bacterial resistance in the population. The primary goal of this project is to provide a proof of concept for the identification of 50 to 100 microbial species found in more than 99% of all urinary tract infections (UTI), one of the most common infections in humans. In the next steps, the method will be validated in a clinical context and could also be extended to other types of infections. In the end, MICROB-AI could revolutionize the clinical diagnosis in microbiology in the next years.
Lead Genome Centre: Génome Québec
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| Mary | Blackburn | Thermo Fisher Scientific – Evosep |