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.

美国食品药品监督管理局（FDA）已将提高医疗器械非临床评价的预测价值确定为研究重点。在与FDA批准医疗器械相关的许多情况下，计算建模可以补充或取代实验室研究、动物试验或临床试验。其潜力尚未完全实现，因为在开发或评估医疗器械时使用计算模型还没有标准化或行业认可的工作流程或评估指标。该项目的目标是将计算建模和模拟从科学研究工具提升为高质量的监管工具。增加计算证据的作用将加速医疗器械的创新和监管决策。该项目还将增加有关可重复计算的知识基础，并提高审查人员处理计算证据的舒适度。将这些知识传达给行业将提高器械提交的质量。监管级计算建模的长期、透明度和可再现性标准可以导致文化的改变，在这一领域建立对计算证据的更多信任。这个为期一年的项目是一个概念验证工作流程和试点研究，旨在为医疗器械安全性和有效性的监管非临床评价生成值得信赖的计算证据。该项目的概念验证应用程序是一个电子药物输送系统（EDDS）原型，一个基于电子烟设备的电子吸入器。在监管科学的背景下，这项研究建立在，适应，并扩大了可重复性的框架，现在成为建立在元研究。在此过程中，这项研究支持监管机构和行业了解EDDS原型的严格和值得信赖的计算证据，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。该试验性研究为监管科学、计算机科学、网络物理系统和医疗器械领域的文献做出了贡献。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。