Using Machine Learning to Anticipate Antimicrobial Resistance for Pathogen Surveillance and Therapeutic Stewardship

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Antimicrobial resistance (AMR) is a serious global health challenge, for both human and animal health. Antimicrobials are drugs that are used to fight pathogenic microbes (viruses, bacteria and parasites). The widespread use and misuse of these drugs has led to the emergence of resistance. One of the ways by which microbes become resistant to drugs and survive drug exposure is by undergoing genetic changes (mutations). There is an urgent need to identify and predict such mutations that lead to resistance before they become fixed in a given pathogen population. Technological advancements have led to rapid, cost effective, and extensive data collection in all aspects of health science. However, to fully exploit these information rich sources, it becomes imperative that corresponding computation techniques are also developed to enhance our understanding of complex biological problems. Artificial Intelligence through Machine learning (ML) offers a powerful way to expedite our understanding of these problems. As the name suggests, ML is a way to make computers learn from and discover patterns in data. The project aims to develop a robust and automated computational framework to identify new mutations in pathogens. Understanding the molecular consequences of these mutations will provide insight into the growing failure of antimicrobial therapy. Developing sophisticated machine learning approaches will accelerate the identification of new ways to detect and respond to AMR. This will have a direct impact in pathogen surveillance, aid stewardship and guide development of new interventions. This multi-disciplinary project integrates and enhances skills in maths, statistics and computation (BBSRC core skills - new ways of working) to leverage information from existing vast and diverse data sources. This will allow expansion and advancement of the vital skills necessary to promote the UK's bioscience research capabilities and bioeconomy (BBSRC core skills - vulnerable capabilities). Combatting AMR spans multiple BBSRC strategic priorities; the project focuses on understanding the basic bioscience of molecular variation (Strategic research priority 3 - bioscience for health) as applicable to AMR, thereby supporting improvements in both human and animal health (Strategic research priority 1 - agriculture and food security).