New treatments for Alzheimer’s disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. A team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence-based method to screen currently available medications as possible treatments for Alzheimer’s disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating, neurodegenerative condition and could reveal new, unexplored targets for therapy by pointing to mechanisms of drug action.

In an article published in Nature Communications, Artem Sokolov, Ph.D., and his colleagues describe their framework, called DRIAD (Drug Repurposing in Alzheimer’s Disease), which relies on machine learning, a branch of artificial intelligence in which systems are “trained” on vast amounts of data, “learn” to identify telltale patterns, and augment researchers’ and clinicians’ decision-making. DRIAD works by measuring what happens to human brain neural cells when treated with a drug. The method then determines whether the changes induced by a drug correlate with molecular markers of disease severity.

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