Study Rationale: Parkinson’s disease (PD) affects millions of people and currently has no treatment that can slow or stop its progression. While large genetic and molecular studies have uncovered many changes in the brains of people with PD, it remains unclear which of these changes can be targeted safely and effectively with drugs. To address this gap, our project uses artificial intelligence to evaluate and prioritize the “druggability” of these PD-related target genes — that is, how likely they are to respond to medicines without harmful effects. By linking these findings with large-scale real-world medication databases, we aim to identify promising and therapeutic drug targets for PD prevention and progression.
Hypothesis: We hypothesize that integrating multi-omics data with AI-based druggability modeling will reveal specific molecular targets that can be safely modulated by existing drugs to prevent or slow Parkinson’s disease progression, bridging the gap between biological discovery and real-world therapeutic development.
Study Design: We will combine multi-omics data from human brain tissue to identify genes consistently altered in Parkinson’s disease. Using AI algorithms, we will calculate each gene’s “druggability”, or how likely it is to be safely targeted by a drug. Then, we will search for existing medications that act on these targets and test their protective effects in large real-world medication databases. This approach allows us to connect molecular discoveries in the lab to potential therapeutic options already available in the clinic, accelerating therapeutic development for PD patients.
Impact on Diagnosis/Treatment of Parkinson’s disease: This project will identify promising drug targets and repurposable medications that could slow or prevent Parkinson’s disease progression. By combining molecular discoveries with real-world medication data, it provides a clear and efficient path toward developing disease-modifying therapies for people living with PD.
Next Steps for Development: Validated targets and drugs identified in this study will move to laboratory testing and early clinical evaluation. These results will guide the design of future trials and collaborative studies, accelerating the discovery of new, effective treatments to improve outcomes for individuals with Parkinson’s disease.