Anna Pahl

PhD thesis

AI-driven Discovery and Testing of Senolytic Compounds for Parkinson’s Disease in Organoid Model

The project involves multimodal analysis of organoid data to characterise Parkinson’s disease (PD) pathology. Single nucleotide polymorphisms in several genes, including LRRK2, are known risk factors for sporadic PD, along with environmental factors like ageing. Associations between PD development and ageing are obvious (PD is primarily a disease of old age) but underexplored– especially how ageing affects LRRK2 G2019S-associated pathology. This study hypothesizes that cellular senescence is a trigger and amplifier of this pipeline from LRRK2 G2019S to PD and that senolytic therapies may therefore treat age-associated PD and other neurodegenerative disorders. This project will explore senolytic therapeutics in a PD organoid model, employing machine learning models trained on data from literature to identify which compounds offer the most promising therapy. Tests will then assess how the screened therapeutics impact senescence, molecular ageing, and mechanistic pathology. The top three senolytics will be compared to state of the art LRRK2
drugs.

Who or what inspired you to pursue your research interests?

Genes associated with Alzheimer’s disease risk run in my family, and I have witnessed firsthand how this disease affects people both directly and indirectly, from memory loss, to managing care and expenses, to grief. Indirectly experiencing the effects of Alzheimer’s disease and similar neurodegenerative disorders, and knowing that over 55 million people worldwide are directly affected, not to mention the other hundreds of millions of loved ones like me who are also indirectly caught in this web of terminal, incurable disease is my “why.” To give people more time, and better time, with those they love is the ultimate goal.