Research News

Cleveland Clinic researchers created an AI-based tool for analyzing vast amounts of genetic data related to Alzheimer’s disease, opening up opportunities for target identification and drug discovery.

The tool, which is publicly available online, is designed to push forward therapeutic discovery for Alzheimer’s disease, the most common form of dementia expected to affect more than 150 million people by 2050. A study published in Cell Reports prioritizes 156 risk-associated genes through the model, which is based in deep learning technology. Deep learning is where a program “learns” from large amounts of data to more effectively identify and analyze information.

Developing AI technology-based tools is essential for taking advantage of genomic sequencing data as Alzheimer’s disease rates continue to climb, says Feixiong Cheng, PhD, Genomic Medicine Institute.

“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 Alzheimer’s disease,” he says. “That allows us to identify targets for treatment development.”

Genetic sequencing data is becoming more readily available through projects like the Alzheimer’s Disease Sequencing Project funded by the National Institute on Aging (NIA), a  center at the National Institutes of Health (NIH). These NIH/NIA-funded AI and machine learning programs share genetic and genomic information to aid research efforts for accelerating Alzheimer’s treatment development, Dr. Cheng says.

This study integrated information from large-scale Alzheimer’s disease genetic databases and a Cleveland Clinic-developed interactome, which describes fundamental interactions between proteins in the human cells. Researchers used the “network topology-based deep learning framework to identify potential drug targets and repurposable treatments for Alzheimer’s disease” or “NETTAG,” to analyze the data.

The genetic sequencing information from public databases provides data on which genes are likely to be associated with Alzheimer’s disease. Connecting this information with the interactome then identifies the biological pathways associated with these genetic markers, which drugs can target.

Studying drug targets for prevention and treatment

Researchers chose four drugs for further screening based on the patient analysis: ibuprofen, gemfibrozil, cholecalciferol and ceftriaxone. The study outlines multiple methods for gauging the potential of each drug: verifying outcomes against millions of patients’ electronic health records; comparing similar drugs in clinical trials; and analyzing race or sex-specific outcomes.

Those analyses identified gemfibrozil, a cholesterol medication prescribed to reduce blood fat levels, as a strong candidate drug for potential prevention and treatment of Alzheimer’s disease.

“Patient records showed gemfibrozil is associated with significantly lower Alzheimer’s disease risk, and we have the genetic and protein-protein data to show it targets molecular pathways associated with the disease,” Dr. Cheng says. “This method provides strong evidence to rapidly identify candidate drugs for clinical trials, but it can also serve as validation for drugs identified through other methods, accelerating the drug discovery process.”

Funded by the National Institute on Aging, Dr. Cheng’s team are developing and utilizing AI and deeplearning technologies towards the creation of innovative tools capable of analyzing 10,000+ sequenced whole-genomes available from the Alzheimer’s Disease Sequencing Project. They will then use the AI tools to identify novel drug targets and molecular networks involved in AD as well as genetic evidence-supported repurposable medicines.

Jielin Xu, PhD, a postdoctoral fellow in Dr. Cheng’s lab is the first author on the study. These findings have been presented at the Alzheimer's Association International Conference 2022 (AAIC22), July 31- Aug. 4, 2022, San Diego. The study was primarily supported by the NIA.