With a new $4 million grant, Drs. Cheng, Bekris and Leverenz will develop and utilize artificial intelligence tools to identify novel drug targets and repurposable drugs for Alzheimer’s disease.
A multidisciplinary research team at Cleveland Clinic has been awarded a five-year, $4 million grant from the National Institute on Aging, part of the National Institutes of Health, to develop artificial intelligence (AI) tools that can identify novel drug targets for Alzheimer's disease (AD).
Alzheimer's disease remains the leading cause of dementia and the sixth leading cause of death in the United States and is expected to affect 13.8 million Americans by 2050 if disease-modifying treatments that slow the neurodegenerative process are not established. Unfortunately, the attrition rate for AD clinical trials is estimated at over 99 percent and genetic risk for AD is very complex, resulting in lack of druggable targets for therapeutic development.
The team will be led by corresponding principal investigator (PI) Feixiong Cheng, PhD, assistant staff in the Genomic Medicine Institute, and co-PIs Lynn Bekris, PhD, associate staff in the Genomic Medicine Institute, and James Leverenz, MD, director of the Cleveland Lou Ruvo Center for Brain Health and director of the Cleveland Alzheimer's Disease Research Center.
"Therapeutic approaches that are guided by a patient's specific makeup should be much more effective than one-size-fits-all approaches. But, the existing genetic and genomic data available to us have not yet been fully utilized to explore targeted therapeutic development for AD, due in large part to the limitations of traditional analysis methods," said Dr. Cheng. "However, advances in capable and intelligent computer-based algorithms offer the opportunity to harness large-scale data to pinpoint functional variants and risk genes that drive AD."
With this grant, the team will develop and utilize AI and deep learning methodologies towards the creation of innovative tools capable of analyzing 10,000 sequenced whole-genomes/exomes available from the Alzheimer's Disease Sequencing Project, also funded by the National Institute on Aging. They will then use the tools to identify novel drug targets and molecular networks involved in AD as well as biologically relevant repurposable drugs.
"This investigation will be conducted by interdisciplinary investigators with diverse and complementary expertise that altogether guarantees completion of this project, including geneticists, bioinformaticians, statisticians, clinicians, cognitive systems specialists and drug discovery experts," said Dr. Cheng. "The completion of this project may accelerate the translation of human genome findings for the emerging development of precision medicine-based, disease-modifying therapeutic approaches for AD."
Others collaborators on the project include Shaun Stauffer, PhD, director of the Center for Therapeutic Discovery; John Barnard, PhD, associate staff in the Department of Quantitative Health Sciences; and Ming Hu, PhD, assistant staff in the Department of Quantitative Health Sciences.
The open-source artificial intelligence (AI) technology uses human genetic data to identify candidate drugs.
Analyzing data from more than 5 million patients revealed telmisartan is associated with lower Alzheimer’s disease incidence in Black patients, providing clues for potential treatment and prevention.
Dr. Cheng’s team developed an artificial intelligence methodology to uncover molecular targets involved in neuroinflammation and identify candidate therapeutics for Alzheimer’s disease.