RFP – Innovation in Regulatory Science
BWF’s Innovation in Regulatory Science Awards provides $500,000 over five years to academic researchers developing new methodologies or innovative approaches in regulatory science that will ultimately inform the regulatory decisions FDA and others make
Application Deadline: February 10, 2023
Finalist Notified: End of April 2023
Notice of Award: July 2023
Awards Begin: September 1, 2023
The Burroughs Wellcome Fund (BWF) recognizes Regulatory Science as an important yet underfunded area of research.
With this initiative, BWF aims to provide research support to stimulate innovation in this area. The process of translating biomedical discoveries into new therapies has become increasingly complex in light of evolving science and technology, and requires that the science of regulation keep up with the advances in biomedical science and technology. For example, existing animal models of human disease are often poor predictors of efficacy of new therapeutic approaches in humans. As
new technologies produce new types of preclinical models, innovation is needed in the evaluation of these models to justify movement into clinical studies. Although numerous reports have documented the importance of this area of research to the future of the biomedical enterprise, it remains inadequately supported.
Regulatory science has been defined as “the science of developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of FDAregulated products.” Regulatory science has become a centerpiece of the Food and Drug Administration’s (FDA) strategy for fostering innovation, and the academic and foundation communities have been called to take an active role in building this emerging field. We therefore strongly encourage investigators to address regulatory science in areas of the FDA’s strategic priorities including but not limited to: regenerative medicine (including gene therapy), artificial intelligence and machine learning, digital health, reduction of animal testing, and model-informed product development.