Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

TLDR

Clinical trials of novel therapeutics for Alzheimer’s disease have consumed significant time and resources with largely negative results, whereas repurposing FDA‑approved drugs offers a faster and cheaper alternative. The study introduces DRIAD, a machine‑learning framework that quantifies associations between AD Braak stage severity and gene‑list encoded molecular mechanisms. DRIAD analyzes gene lists from drug‑treated neural cell cultures of 80 FDA‑approved drugs, ranks potential repurposing candidates, and examines top hits for shared target trends. The authors suggest that DRIAD can nominate drugs for clinical evaluation after further validation and biomarker identification.

Abstract

Abstract 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.