Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.
Date: 12 October 2021, 15:00 (Tuesday, 1st week, Michaelmas 2021)
Venue: virtual via Zoom
Speaker: Professor Artem Sokolov (Harvard University)
Organising department: Department of Psychiatry
Organiser: Dr Andrey Kormilitzin (University of Oxford)
Organiser contact email address:
Host: Dr Andrey Kormilitzin (University of Oxford)
Part of: Artificial Intelligence for Mental Health Seminar Series
Booking required?: Not required
Audience: Public
Editor: Andrey Kormilitzin