Revolutionising antimicrobial susceptibility testing to optimise treatment of bacterial infection and overcome AMR

About the Project

Antimicrobial susceptibility testing (AST) is an essential component of the diagnostic workflow in the clinical microbiology laboratory, since it is able to define which antibiotics have the potential to treat bacterial infection in a given patient. Current AST systems can determine antibiotic susceptibility profiles in ~6-8 hours under ideal conditions. In cases of serious, life-threatening infection (e.g. bloodstream infection), that time window is too long to delay treatment, and patients are prescribed broad-spectrum antibiotics whilst awaiting AST results. The delay in achieving a definitive answer regarding appropriate antibiotic treatment has multiple negative outcomes – not only for the patient, who might be receiving inappropriate treatment or suffering damage to their microbiota from the administration of broad-spectrum drugs – but also by driving the emergence of antibiotic resistance in the patient and beyond. Thus, novel approaches to AST that dramatically shorten the timeframe to actionable results would have far-reaching benefits on the treatment of bacterial infection.

Classical AST involves visual detection of bacterial growth in the presence of antibiotics. In some of the automated AST systems, such visual detection is further enhanced using lasers or redox-sensitive dyes, but the basic concept remains the same. Alternative detection methods have been proposed in the literature (PubMed ID: 32760676), but these are at best some way from application, often offer only limited or uncertain benefit in terms of reducing the timeframe to an AST profile, or appear unworkable for large-scale, routine deployment in the diagnostic laboratory.

The O’Neill laboratory – in collaboration with Roboscientific (https://www.roboscientific.com/technology/) – has generated proof of principle for a new AST approach that can discriminate antibiotic resistant from antibiotic susceptible strains in as little as 60 minutes (and potentially far less with further optimisation). This approach works by detecting the volatile organic compounds (VOCs) that bacteria generate as they grow - and which will not be present when the growth of antibiotic-susceptible bacteria is inhibited by the presence of an antibiotic. Though the concept of using VOC detection in this context is not entirely without precedent (PMID: 32760676), the only current system that uses this approach employs optical sensors as the detectors (https://specificdx.com/product/specific-reveal/) and takes ~5.5 hours to produce an AST profile. Based on our expertise in AST in conjunction with Roboscientific’s proprietary electrochemical VOC detection technology, this project will build on our initial results to evolve, optimise and validate this approach to deliver a dramatic improvement on the current state of the art and to thereby revolutionise AST. This is an exciting project capable of delivering real-world impact, and will provide the appointed student with cutting-edge, multidisciplinary training in molecular bacteriology, VOC sensing, AST and allied disciplines.

Please see the O’Neill lab website for more information about what we do, and links to our published work: https://biologicalsciences.leeds.ac.uk/molecular-and-cellular-biology/staff/119/professor-alex-o-neill