NSF-FDA: Generating trustworthy computational evidence to support FDA’s regulatory evaluation of medical devices, via transparency and reproducibility

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The Food and Drug Administration (FDA) has identified as a research priority the need to improve the predictive value of non-clinical evaluation of medical devices. Computational modeling can complement or replace laboratory studies, animal trials, or clinical trials in many situations relating to FDA approval of medical devices. Its potential has yet to be fully realized because no standardized or industry accepted workflow or appraisal metrics exist for using computational models in developing or assessing medical devices. The goal for this project is to elevate computational modeling and simulation from scientific research tools to high-quality regulatory tools. Increasing the role of computational evidence will accelerate innovation and regulatory decision-making of medical devices. The project will also increase the knowledge base about reproducible computations, and increase the level of comfort for reviewers handling computational evidence. Communicating this knowledge to industry will increase the quality of device submissions. Longer term, transparency and reproducibility standards in regulatory grade computational modeling can lead to a change in culture, building more trust in computational evidence in this sphere.This one-year project is a proof-of-concept workflow and pilot study for generating trustworthy computational evidence for regulatory non-clinical evaluation of medical device safety and efficacy. The proof-of-concept application for this project is an electronic drug delivery system (EDDS) prototype, an electronic inhaler based on e-cigarette devices. This study in the context of regulatory science builds on, adapts, and expands a framework of reproducibility, now becoming established in meta-research. In the process, this study supports regulators and industry in understanding rigorous and trustworthy computational evidence for the EDDS prototype, drawing upon computational fluid dynamics models and best practices for digital evidence. The provenance of all the research artifacts and empirical results will be rigorously captured, recorded, and curated including, for example, data, code, parameters, computational environment, and computational workflow. This pilot study contributes to the literature in regulatory science, computer science, cyber-physical systems, and medical devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.