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

Steve Rodriguez, Clemens Hug, Petar Todorov, Nienke Moret, Sarah A. Boswell, Kyle Evans, George Zhou, Nathan T. Johnson, Bradley T. Hyman, Peter K. Sorger, Mark W. Albers () and Artem Sokolov ()
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Steve Rodriguez: Harvard Medical School
Clemens Hug: Harvard Medical School
Petar Todorov: Harvard Medical School
Nienke Moret: Harvard Medical School
Sarah A. Boswell: Harvard Medical School
Kyle Evans: Harvard Medical School
George Zhou: Harvard Medical School
Nathan T. Johnson: Harvard Medical School
Bradley T. Hyman: Massachusetts General Hospital
Peter K. Sorger: Harvard Medical School
Mark W. Albers: Harvard Medical School
Artem Sokolov: Harvard Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-13

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.

Date: 2021
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DOI: 10.1038/s41467-021-21330-0

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