Researchers at the Cleveland Clinic Genome Center (CCGC) have identified potential genetic factors that could play a role in Parkinson’s disease (PD), and have identified FDA-approved drugs that could be repurposed as new PD treatments. The research, published in the journal npj Parkinson’s Disease, used an artificial intelligence (AI) and systems biology approach primarily examining non-coding regions of the genome to improve the understanding of the genetic underpinnings of PD.
“Parkinson’s disease is the second most common neurodegenerative disorder, right after dementia, but we don’t have a way to stop or slow its progression in the millions of people who live with this condition worldwide; the best we can currently accomplish is managing symptoms as they appear,” said Lijun Dou, PhD, the study’s first author and a postdoc in the lab of Feixiong Cheng, PhD a leading systems biology researcher.
The Cleveland Clinic team used an integrative approach leveraging AI to analyze data from genetic, proteomic, pharmaceutical, and patient datasets. Their AI model cross-referenced Parkinson’s-related genetic variants with a number of different brain-specific DNA and gene expression databases. By examining noncoding regions of DNA—those areas not directly coding for proteins but influencing gene function—the researchers pinpointed several genes that could be involved in PD progression.
Among the identified risk genes were SNCA and LRRK2, both of which are known to play roles in brain inflammation when mutated. The researchers found that these genes exhibit specific functional traits and are highly expressed in the brain areas affected by Parkinson’s.
In addition to identifying new genetic risk indicators, the team sought to find if any existing FDA-approved drugs could be repurposed to treat Parkinson’s disease.
“Individuals currently living with Parkinson’s disease can’t afford to wait that long for new options as their conditions continue to progress,” said senior author Cheng, who is director of the CCGC. “If we can use drugs that are already FDA-approved and repurpose them for Parkinson’s disease we can significantly reduce the amount of time until we can give patients more options.”
The research team cross-referenced their findings with pharmaceutical databases to identify drugs to target the genes they identified. One drug that emerged from this search was simvastatin, a cholesterol-lowering medication. People who had been prescribed simvastatin were found to be less likely to develop Parkinson’s disease, a finding that aligns with previous research suggesting statins offer neuroprotective benefits by targeting inflammatory and lysosomal pathways (mechanisms that play a central role in neurodegeneration).
The researchers are now planning laboratory tests to evaluate the potential of simvastatin and other promising drugs, such as immunosuppressive and anti-anxiety medications, to slow or modify the progression of Parkinson’s disease.
“Using traditional methods, completing any of the steps we took to identify genes, proteins and drugs would be very resource- and time-intensive tasks,” Dou noted. “Our integrative network-based analyses allowed us to speed this process up significantly and identify multiple candidates which ups our chance of finding new solutions.”
The team also plans to expand the scope of their work by incorporating larger, more diverse genetic datasets to potential identify other risk genes, and further exploring the repurposing of other drugs identified using their AI tool. Ultimately, their work could help streamline the process of finding new Parkinson’s disease treatments.
“Discovery of therapeutic approaches by specifically targeting genetics-informed risk genes is essential for developing disease-modifying treatments for PD,” the researchers wrote in their study.
