Harvard researchers have used machine learning to find molecular features in existing drugs that may be effective in warding off or treating Alzheimer’s disease.
Calling the system DRIAD for drug repurposing in Alzheimer’s Disease, the team presents its work in Nature Communications.
“DRIAD is distinct from the traditional approaches in which a [computational] model is constructed over the entire gene space and subsequently interrogated for feature importance scores and the enrichment of predefined gene sets, which then serve as a candidate list for further functional studies,” write Artem Sokolov, PhD, and colleagues. “DRIAD effectively decouples gene set enrichment and predictor performance by filtering the transcriptomic space for genes associated with drugs prior to model training and predictor evaluation” in clinical investigations.
In coverage by the Harvard Gazette, Sokolov puts the significance of the advance in lay terms:
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. We therefore built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones.
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.