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Developing A Resource for Exploring the Molecular Markers of Parkinson’s Disease

Study Rationale: Our understanding of the genetic, immunological and molecular determinants of Parkinson’s disease (PD) has advanced significantly over the past decade. Using this extensive body of scientific evidence, we can begin translating these insights into products that can improve the clinical management of PD. In particular, we would like to shrink the latent period of PD: the time between symptom onset and formal clinical diagnosis. This study proposes to leverage the biospecimen repository collected by the Personalized Parkinson Project to generate a publicly available molecular data resource that can be used to accelerate diagnostic and therapeutic developments for managing PD.

Hypothesis: Developing a publicly available data resource will accelerate the generation of insights into the mechanisms and signatures of PD, with a particular emphasis on producing data that can improve our understanding of PD genetics, immunology and metabolism.

Study Design: The Personalized Parkinson Project is a two-year study that included collection of detailed clinical histories and biospecimens from people with PD. Now, we will leverage cutting-edge molecular profiling techniques and combine molecular data with clinical outcomes to generate a public resource that can accelerate the investigation of a range of scientific questions.

Impact on Diagnosis/Treatment of Parkinson’s disease: The proposed work aims to accelerate the development of products that can improve the diagnosis of PD, improve our ability to monitor disease progression, and enhance our understanding of disease mechanisms that could potentially be targeted therapeutically.

Next Steps for Development: Academic and industry investigators will be able to leverage these data to generate new hypotheses and novel insights, and to validate the findings of other studies. Success will be gauged by the commercial launch of diagnostics and therapeutics whose development was directly accelerated by the data from this proposed resource.


  • Charlie Kim, PhD

    South San Francisco, CA United States

  • Robert Scooter Plowman, MD, MBA, MHSA, MSc

    Thousand Oaks, CA United States

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