Identifying drug-vaccine interactions in electronic health record data
COLLABORATORS: Prof William Henley (Health Statistics Group, CMH), Prof Mark Kelson (CEMPS; Turing Fellow), Prof Sebastian Vollmer (University of Warwick; Turing Institute), Dr John Dennis (CMH), Dr Lauren Rodgers (CMH), Dr Adam Streeter (Münster Universitätklinikum), Dr Gus Hamilton (NIHR Academic Clinical Fellow, North Bristol NHS Trust).
IDSAI Research Fellow: Dr Oliver Stoner
Description: As progress is made towards developing vaccines against COVID-19, there is increasing interest in understanding ways of enhancing the effectiveness and safety of both new and existing vaccines. Understanding ways of enhancing vaccine response has the potential to have a major impact on tackling morbidity and mortality from existing and emerging infectious diseases in vulnerable populations.
Some of the most common prescription drugs in the UK are medications to treat high blood pressure, statins to treat or delay cardiovascular disease, proton pump inhibitors for heart-burn and acid related disorders, metformin to delay or treat diabetes and non-steroidal anti-inflammatory drugs. A number of these medications are also known to have modulating effects on the immune system that could either inhibit or enhance the immune response when patients receive routine vaccinations. The project team aims to use machine learning approaches to facilitate a data-driven exploration of drug-vaccine interactions to identify candidates for future investigation. This data-intensive screening approach has the potential for rapid deployment at scale to validate interactions with known mechanisms as well as contributing to the discovery of new targets. This proposed project combines methodology development with delivering clinically-relevant results for influenza vaccination.
