ERADIAMR

Antimicrobial resistant bacteria are one of the major health challenges of the 21st century. In 2019 alone, nearly 5 million deaths were associated with failure to treat infections caused by resistant bacteria. There is a clear and urgent need to find new ways to detect antimicrobial resistant bacteria more rapidly and more effectively. The ERADIAMR project will develop a new way of detecting antimicrobial resistant bacteria by looking at the way live bacteria vibrate. In fact, we have recently discovered that when bacteria are treated with a specific antibiotic drug, they vibrate in a different way depending on whether they are susceptible or resistant to the antibiotic in use. Sensing these vibrations requires only 4 hours and therefore will allow us to understand very quickly which antibiotic needs to be used to cure an infection. In contrast, currently it can take longer than a day for a physician to decide which antibiotic should be used with current standard methods. In this project we will look at the vibrations of bacteria that typically infect humans and against which there is an urgent need to find the best antibiotic treatment. Therefore, the new technology that we will develop will allow in the future to rapidly detect and treat infections due to resistant bacteria and save lives.

A timely detection of antimicrobial resistance (AMR) is crucial in several medical conditions, including sepsis that causes 11 million deaths per year world-wide. The ERADIAMR project will develop effective and rapid diagnostics of AMR in clinical isolates by harnessing the power of a novel interdisciplinary multi-technology approach. We will combine whole genome sequencing and conventional antibiotic susceptibility testing (AST) with two emerging and rapid AST technologies, namely nanomotion technology platform and single-cell microfluidics-microscopy. We will apply this new approach to a large collection of clinical isolates of the so called ESKAPE pathogens that are key players in infections, such as sepsis, and are becoming increasingly resistant to conventional antibiotic treatment. By integrating and cross-validating the multi-technology data that we will acquire, we will aim to develop and implement a rapid and effective diagnostic platform that will perform AST and detect AMR within 4 hours and will predict the optimal antibiotic drug and adequate dose for each specific infecting bacterial strain. As such this project will pose a steppingstone for future rapid diagnostics and high throughput, personalised antibiotic treatment optimisation to rapidly overcome AMR.