Precompetitive Data Sharing as a Catalyst to Address Unmet Needs in Parkinson’s Disease

Journal article

Parkinson’s disease is a complex heterogeneous disorder with urgent need for disease-modifying therapies. Progress in successful therapeutic approaches for PD will require an unprecedented level of collaboration. At a workshop hosted by Parkinson’s UK and co-organized by Critical Path Institute’s (C-Path) Coalition Against Major Diseases (CAMD) Consortiums, investigators from industry, academia, government and regulatory agencies agreed on the need for sharing of data to enable future success. Government agencies included EMA, FDA, NINDS/NIH and IMI (Innovative Medicines Initiative). Emerging discoveries in new biomarkers and genetic endophenotypes are contributing to our understanding of the underlying pathophysiology of PD. In parallel there is growing recognition that early intervention will be key for successful treatments aimed at disease modification. At present, there is a lack of a comprehensive understanding of disease progression and the many factors that contribute to disease progression heterogeneity. Novel therapeutic targets and trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination are required. The integration of robust clinical data sets is viewed as a powerful approach to hasten medical discovery and therapies, as is being realized across diverse disease conditions employing big data analytics for healthcare. The application of lessons learned from parallel efforts is critical to identify barriers and enable a viable path forward. A roadmap is presented for a regulatory, academic, industry and advocacy driven integrated initiative that aims to facilitate and streamline new drug trials and registrations in Parkinson’s disease.

Authors: Stephenson, D; Hu, M; Romero, K; Breen, K; Burn, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOS Press
• Journal: Journal of Parkinson's Disease
• Volume: 5
• Issue: 3
• Pages: 581-594
• Publication date: 2015-09-14
• DOI: 10.3233/jpd-150570
• EISSN: 1877-718X
• ISSN: 1877-7171
• Pmid: 26406139
• Language: English
• Keywords: privacy; SBTMR; quantitative disease progression; data integration; regulatory science; collaboration; data standards