Researchers have uncovered how immune cells known as microglia can change and contribute to damaging conditions like neuroinflammation in Alzheimer’s disease. Their study, published in Alzheimer’s & Dementia: The Journal of The Alzheimer’s Association, combines drug databases with real-world patient data to pinpoint existing FDA-approved medications that could be repurposed to specifically target microglia affected by the disease while sparing healthy cells.
Cleveland Clinic Genome Center researchers have uncovered how immune cells called microglia can change and contribute to damaging conditions like neuroinflammation in Alzheimer’s disease. The study, published in Alzheimer’s & Dementia: The Journal of The Alzheimer’s Association, merges drug databases with real-world patient information to find FDA-approved medications that may be repurposed to specifically target disease-related microglia without impacting the healthy variants.
The research team, led by author Feixiong Cheng, PhD, hopes that their innovative method of combining genetic, chemical, and human health data to discover drug targets and related medications will motivate other scientists to adopt similar strategies in their own studies.
Microglia are specialized immune cells that monitor our brains for tissue damage and threats like bacteria and viruses. Different types of microglia utilize various approaches to protect the brain. Some initiate neuroinflammation—an inflammatory response in the brain—to combat adversaries or stimulate repair in harmed cells. Others may actively eliminate harmful substances and clear away damage and debris. However, in Alzheimer’s disease, new forms of microglia can develop that facilitate the progression of the disease.
“Microglia have been linked to Alzheimer’s disease for over a century. Attempts to halt disease progression using broad-spectrum anti-inflammatory medications and ‘harmful’ microglial inhibitors have not succeeded,” explains Dr. Cheng, director of the Cleveland Clinic’s Genome Center. “We need to specifically target detrimental microglial subtypes while preserving the normal, healthy types.”
Dr. Cheng notes that the factors leading to the formation of these harmful microglial subtypes, as well as how they operate, remain largely unknown.
To create a more targeted drug for harmful microglia, Dr. Cheng and his lab posed the following questions:
- What molecular differences separate harmful microglia from their normal, beneficial counterparts?
- What existing drugs can specifically target these differences to halt or reverse the formation of harmful microglia?
- If multiple promising drugs were identified, which one showed the most potential? Is there any evidence that these drugs could be effective in humans?
Answering these questions required various types of data. To efficiently combine and analyze extensive datasets for computational evaluation, Dr. Cheng gathered a team to adopt a comprehensive “network-based” approach.
The team collaborated with experts from IBM, Weill Cornell Medicine, Case Western Reserve University, the Cleveland Clinic Lou Ruvo Center for Brain Health, and the University of Nevada, Las Vegas, to help interpret their data.
Under the guidance of lead author Jielin Xu, PhD, the team developed an algorithm to merge and analyze:
- RNA-sequencing datasets from over 700,000 individual cells affected by Alzheimer’s, which helped identify distinct characteristics of harmful microglia based on gene activity.
- Protein-protein interaction data from 18 publicly available datasets, predicting how proteins unique to harmful microglia influence cellular functions.
- Chemical and drug databases to discover which FDA-approved medications might disrupt disease-specific protein interactions linked to harmful microglial activity.
- Real-world patient data from millions of insured individuals to assess whether any identified drugs correlated with reduced rates of Alzheimer’s diagnoses.
“Our study provides a robust deep generative model for identifying repurposable drugs from various Alzheimer’s findings, and the methods can also be applied to other ailments,” remarks Dr. Cheng.
The team’s analyses identified three distinct subtypes of harmful microglia that contributed to disease progression, each with its own genetic profile driving unique behaviors related to Alzheimer’s. For instance, one subtype triggers harmful neuroinflammation, while another facilitates the aggregation of proteins, such as tau, that are associated with Alzheimer’s.
Each subtype exhibited unique genetic signatures that led to their transformation from protective to harmful roles. Further investigation into these harmful microglia and their genetic profiles could unveil additional drug targets and improve treatment options for Alzheimer’s.
The findings indicated that several FDA-approved drugs could already be used to inhibit detrimental transitions in microglia. Repurposing these medications for Alzheimer’s treatment offers a safer and faster alternative to developing entirely new drugs, according to Dr. Xu.
The research also revealed that patients taking one of the potentially repurposable drugs, an NSAID known as Ketorolac, used for treating mild to moderate pain, were diagnosed with Alzheimer’s at a lower rate than those who did not use the drug. Experimental validation revealed that Ketorolac blocked type-I interferon (IFN) signaling, an immune response process, in microglia derived from Alzheimer’s patients. The team plans to conduct further experimental and clinical studies to evaluate Ketorolac’s impact on Alzheimer’s disease.
Dr. Cheng emphasizes that, although their analysis primarily focused on Alzheimer’s, the implications of their findings could extend to various other neurodegenerative and age-related complex diseases.
“Previously, each of these discoveries would require an extensive independent research initiative,” Dr. Cheng notes. “Our advanced computational techniques enable biological, chemical, and patient-related breakthroughs within a single study. These artificial intelligence (AI)-assisted network-based analyses represent the future of biomedical research.”
This research was funded by grants from the National Institute on Aging (NIA).
