Elpida Kontsioti

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Elpida graduated from the National and Kapodistrian University of Athens in 2017 with a 5-year pharmacy degree. In September 2017, she joined the Centre for Doctoral Training in Quantification and Management of Risk & Uncertainty in Complex Systems & Environments at the University of Liverpool. Her research lies among drug safety, statistics and computer science, with her project being focused on developing novel algorithms for signal detection of drug-drug interactions. She is co-funded by EPSRC and AstraZeneca.

Research Interests: Signal detection, Pharmacovigilance, Bayesian Statistics

Research project title: Improved Methods for Drug-Drug Interaction Detection

Project description: Adverse drug–drug interactions (DDIs) constitute a major issue in clinical practice and are related to a large number of hospital admissions each year.

Since clinical trials for a drug are necessarily limited in their scale relative to the downstream use of the drug, the safety profile of new medicines cannot be fully determined and understood before they reach the market. Also, the increasing rates of polypharmacy in recent years make the problem even more complex. Thus, post-marketing surveillance is a vital stage in the lifecycle management of a medicine and reporting databases exist in order to collate information about patients who experience adverse events that could be caused by drug combinations. Signal detection algorithms analyse these databases and generate signals (i.e. evidence of potential causal relationship between a specific drug combination and one or more adverse events) that are later assessed for clinical plausibility. In a Bayesian setting, the large size of the hypothesis space when considering potential interactions between large numbers of drugs necessitates efficient numerical techniques. Hence, it would be possible to navigate the space of all the hypotheses relevant to potential DDIs hidden in a database of reports for many drugs. This research will draw upon expertise in signal processing in order to provide a Bayesian approach to identifying potential DDIs, aiming at developing novel algorithms that outperform the existing state-of-the-art and can be used in a real-world setting.

Supervisory team: Professor Simon Maskell, Professor Sir Munir Pirmohamed

ORCID: 0000-0002-4053-0220