Precompetitive Collaborations in Rare Disease Research

When are precompetitive, collaborative models most effective? For rare disease research, at almost every point of the clinical development pipeline, including early preclinical feasibility studies, AI-based modeling to test therapeutic hypotheses, and the administration of natural history studies with patient-approved endpoints.
Data only has value when it is discoverable, shared, and used, but most rare disease data instead exists in silos. By employing a nonprofit health technology model as the neutral convener, we can create robust programs and partnerships to ensure that rare disease data is maximally utilized.
This model benefits every stakeholder in this ecosystem: Patients own and govern their data, leading to heightened research engagement. Rare disease researchers can apply machine learning methods to centralized, large datasets. And drug developers gain access to critical data, enabling more efficient preclinical research across a wider variety of rare diseases.