There is a desperate need to develop new therapies for Alzheimer’s disease, but multiple clinical trials performed on investigational drug candidates have not yielded potential options.
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A research team from Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has now designed an artificial intelligence (AI)–based technique to screen existing drugs and thus determine promising treatments for Alzheimer’s disease.
The AI-based technique may provide a fast and low cost method to repurpose currently available therapies into novel treatments for this debilitating, progressive neurodegenerative disease. Most significantly, the new method may also help uncover new, unexplored targets for treatments by underscoring the mechanisms of drug action.
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.
Artem Sokolov, PhD, Director of Informatics and Modeling, Laboratory of Systems Pharmacology, Harvard Medical School
Sokolov added, “We therefore built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones.”
In a new article published in the Nature Communications journal, Sokolov and his collaborators explained their new framework, known as Drug Repurposing in Alzheimer’s Disease, or DRIAD for short.
This framework depends on machine learning—a field of artificial intelligence, where systems are 'trained' on large amounts of data—'learn'—to detect significant patterns and augment the decision-making of clinicians and researchers.
DRIAD functions by quantifying what happens to the neural cells in the human brain when treated with a certain medication. The technique subsequently establishes whether the variations caused by a medication correspond with the molecular markers of the severity of the disease.
Through this technique, the team was able to detect medications that had both protective and damaging impacts 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.
Steve Rodriguez, PhD, Study Co-First Author and Investigator, Department of Neurology, Massachusetts General Hospital
Rodriguez is also an instructor at HMS.
The DRIAD framework also enables scientists to analyze which kinds of proteins are targeted by the most viable drugs and whether the targets have common trends.
This method was developed by Clemens Hug, PhD, the co-first author of the study and a research associate in the Laboratory of Systems Pharmacology.
The researchers used the screening technique on 80 drugs that were approved by the FDA and clinically tested for a variety of conditions.
The study produced a ranked list of drug candidates, with many anti-inflammatory medications used for treating blood cancers and rheumatoid arthritis that are emerging as top competitors.
Such drugs are part of a group of medications called Janus kinase inhibitors. These medications work by inhibiting the action of inflammation-driving Janus kinase proteins, believed to contribute to Alzheimer’s disease and known for their involvement in autoimmune diseases.
The researchers’ studies also indicated other promising treatment targets for additional analysis.
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 prioritization of these drugs for clinical investigation.
Mark Albers, MD, PhD, Associate Director, Massachusetts Center for Alzheimer Therapeutic Science, Massachusetts General Hospital
Dr. Albers is the Frank Wilkins Jr and Family Endowed Scholar, and also a faculty member of the Laboratory of Systems Pharmacology at HMS.
Dr. Albers will investigate one of these medications, baricitinib, in a clinical trial for patients with mild cognitive impairment, subjective cognitive complaints, and Alzheimer’s disease that will be shortly launched at MGH in Boston and Holy Cross Health in Fort Lauderdale, Florida.
“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,” concluded Albers.
The study was funded by the National Institute on Aging, the CART fund, and the Harvard Catalyst Program for Faculty Development and Diversity Inclusion.
Rodriguez, S., et al. (2021) Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nature Communications. doi.org/10.1038/s41467-021-21330-0.