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

NSF-FDA：通过透明度和可重复性生成值得信赖的计算证据，支持 FDA 对医疗器械的监管评估

• 批准号: 2040175
• 负责人: Lorena Barba
• 金额: $ 5.66万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040175&HistoricalAwards=false
• 关键词: NSF; FDA; Generating; trustworthy; computational

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.

食品药品监督管理局（FDA）已确定为研究优先级，需要提高医疗设备非临床评估的预测价值。在许多情况下，计算建模可以补充或替代与FDA批准医疗设备有关的许多情况下的实验室研究，动物试验或临床试验。它的潜力尚未完全实现，因为在开发或评估医疗设备中使用计算模型没有标准化或行业接受的工作流或评估指标。该项目的目标是将计算建模和仿真从科学研究工具提升到高质量的监管工具。增加计算证据的作用将加速医疗设备的创新和监管决策。该项目还将增加有关可重复计算的知识基础，并增加处理计算证据的审阅者的舒适度。将此知识传达给行业将提高设备提交的质量。监管等级计算建模中的长期，透明度和可重复性标准可以导致文化变化，在该领域建立对计算证据的更多信任。这项一年的项目是概念验证的工作流程和试点研究，用于生成可信赖的计算证据，以生成监管性非临床评估医疗设备安全和效果的监管性非临床评估。该项目的概念验证应用是一种电子药物输送系统（EDDS）原型，这是一种基于电子烟设备的电子吸入器。这项研究在监管科学的背景下建立在，改编和扩展了可重复性的框架，现在已在元研究中建立。在此过程中，本研究支持监管机构和行业，以了解EDDS原型的严格和值得信赖的计算证据，并利用计算流体动力学模型和数字证据的最佳实践。所有研究工件和经验结果的出处将被严格捕获，记录和策划，包括数据，代码，参数，计算环境和计算工作流程。这项试点研究为监管科学，计算机科学，网络物理系统和医疗设备的文献做出了贡献。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。