Faculty Opinions recommendation of Machine learning identifies candidates for drug repurposing in Alzheimer's disease.
Ram Samudrala, Mira Moukheiber · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2021
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. PMID: 33589615 Funding information This work was supported by: NCI NIH HHS, United States Grant ID: U54 CA225088 NIDDK NIH HHS, United States Grant ID: U24 DK116204 NIA NIH HHS, United States Grant ID: R01 AG058063