Development of An Integrated Pipeline for Early PD Prediction and Disease Modifying Drug Repurposing

About This Grant

ABSTRACT Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder all over the world. It is estimated that PD affects 2-3% of people older than 65 years. PD is associated with serious dysfunctions with movement, speech, swallowing, and balance, as well as non-motor symptoms like depression, anxiety, sleep, and cognitive problems, significantly impacting daily activities and ultimately affecting the ability to live independently and maintain a good quality of life. As of now, PD remains pathologically and biologically unclear; and no disease-modifying treatment (DMT) current exist that can slow, halt, or reverse PD disease course. Prior neuropathological evidence indicates that PD pathology may begin up to two decades prior to clinical presentation–40–60% of dopaminergic neurons in the substantia nigra have been lost at time of motor symptom manifesting. Therefore, by the time PD is clinically diagnosed, neurodegeneration may be too advanced to be modified by therapeutic intervention. This has been recognized as a potential reason for the failure of many DMT trials. The pre-diagnostic or prodromal phases of PD (pPD) might represent a critical therapeutic window, during which molecular pathology may still be reversible or more responsive to intervention. In this context, this project aims at targeting the pPD phase to advance development of DMTs for early PD prevention. To this end, Aim 1 will develop an advanced AI-driven framework for harmonizing multimodal health data across PD research datasets, general-purpose biomedical databases, and large-scale EHR, and for building multimodal AI model for early prediction of PD. Aim 2 will develop causal learning-based trial emulation framework to identify and evaluate DMT repurposing candidates in PD, targeting the population with pPD. Aim 3 will leverage PD-derived iPSCs (in vitro) and mouse models (in vivo) for screening and validating the drug repurposing candidates.