"Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School.

"We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.

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For the study, published in Nature Communications, the team described 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. The approach also allowed the researchers to identify drugs that had protective as well as damaging effects on brain cells.

The team applied the screening method to 80 FDA-approved and clinically tested drugs for a wide range of conditions. The analysis yielded a ranked list of candidates, with several anti-inflammatory drugs used to treat rheumatoid arthritis and blood cancers emerging as top contenders.

These drugs belong to a class of medications known as Janus kinase inhibitors. The drugs work by blocking the action of inflammation-fueling Janus kinase proteins, suspected to play a role in Alzheimer's disease and known for their role in autoimmune conditions. The team's analyses also pointed to other potential treatment targets for further investigation.

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