Risk CDT - Improved Detection of Drug-drug Interaction

Scope: Adverse drug–drug interactions (DDIs) harm large numbers of patients every year. Since clinical trials for a drug are necessarily limited in their scale relative to the downstream use of the drug, not all DDIs are known when new medicines are made available to the general public. As a result, reporting databases have been developed to collate information about individuals who experience adverse events that could be side-effects caused by a DDI. Databases of known DDIs exist, but it is possible that reporting databases contain information about other DDIs that are currently unknown to the world and which could be assessed for clinical plausibility (some potential DDIs might look possible from the data but not be plausible from a clinical perspective). Mature algorithms exist to analyse reporting databases. These mature algorithms detect when the number of reports of an adverse event is statistically different when people take a specific single drug relative to when they do not. However, the algorithms for detecting DDIs in the same way are less mature. Recent work in a project involving signal processing (ie Electronic Engineering) and pharmacology (ie Translational Medicine) experts at the University of Liverpool and the Patient Safety team at AstraZeneca has highlighted that existing algorithms for detecting drugs’ side-effects can be reformulated in terms of hypothesis testing. This has enabled existing algorithms to be rearticulated in an explicit Bayesian context, providing a strong foundation for developing new algorithms that consider the detection of DDIs in terms of such Bayesian inference. This is important because it transpires that the hypothesis space will be large when considering potential interactions between large numbers of drugs: to test if one specific set of N drugs (of which there will be very many) are interacting, it appears that there are 2^(2^N-1) hypotheses that need to be considered. To make it feasible to navigate the space of all the hypotheses relevant to potential DDIs hidden in a database of reports for many drugs, there is therefore a need to capitalise on efficient numerical Bayesian inference techniques. The focus of this PhD is to understand the existing state-of-the-art in terms of analysing databases of adverse events before developing, applying, assessing and extending a Bayesian approach to detecting DDIs. The aim is to ensure that the resulting algorithms are readily used by AstraZeneca, as an exemplar pharmaceutical company keen to use algorithms that outperform the existing state-of-the-art in terms of detecting potential DDIs. The successful applicant will end up having skills in statistics, computing and pharmacology. Starting with a strong background in a subset of these disciplines with the enthusiasm to learn about any others is therefore strongly desirable.

Centre for Doctoral Training in Quantification and Management of Risk & Uncertainty in Complex Systems & Environments is funded by EPSRC and ESRC.