AI-based method used to screen for Alzheimer’s disease drugs

A team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), both US, has developed an artificial intelligence (AI)-based method to screen currently available medications as possible treatments for Alzheimer’s disease.

According to the researchers, the method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for the neurodegenerative condition. It could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action.

“Repurposing US Food and Drug Administration (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,” explained Dr Artem Sokolov, at HMS. “We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones.”

The researchers called their framework Drug Repurposing In Alzheimer’s Disease (DRIAD), which relies on machine learning  – a branch of AI in which systems are ‘trained’ on vast amounts of data and learn to identify tell-tale patterns.

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.

“We also approximate the directionality of such correlations, helping to identify and filter out neurotoxic drugs that accelerate neuronal death instead of preventing it,” said co-first author Dr Steve Rodriguez, at HMS.

DRIAD also allowed the researchers to examine which proteins are targeted by the most promising drugs and if there are common trends among the targets.

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-fuelling 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.

“We are excited to share these results with the academic and pharmaceutical research communities. Our hope is that further validation by other researchers will refine the prioritisation of these drugs for clinical investigation,” said Dr Mark Albers, one of the lead researchers.

One of these drugs, baricitinib, will now be investigated in a clinical trial for patients with subjective cognitive complaints, mild cognitive impairment, and Alzheimer’s disease that will be launching soon. 

“In addition, independent validation of the nominated drug targets could provide new insights into the mechanisms behind Alzheimer’s disease and lead to novel therapies,” says Albers.

The study was published in Nature Communications.