CDRH Details Top Regulatory Science Priorities
Posted 22 August 2019 | By
In a report released Thursday, the US Food and Drug Administration’s (FDA) Center for Devices and Radiological Health (CDRH) outlines its top ten regulatory science priorities.
The top ten regulatory science priorities for the center are:
- Leverage “big data” for regulatory decision-making
- Modernize biocompatibility and biological risk evaluation of device materials
- Leverage real-world evidence and employ evidence synthesis across multiple domains in regulatory decision-making
- Advance tests and methods for predicting and monitoring medical device clinical performance
- Develop methods and tools to improve and streamline clinical trial design
- Develop computational modeling technologies to support regulatory decision-making
- Enhance the performance of digital health and medical device cybersecurity
- Reduce healthcare associated infections by better understanding the effectiveness of antimicrobials, sterilization and reprocessing of medical devices
- Collect and use patient input in regulatory decision-making
- Leverage precision medicine and biomarkers for predicting medical device performance, disease diagnosis and progression
The priorities outlined in the report are unchanged from the center’s previous regulatory science priorities list for FY2017, though CDRH says the new report was focused on fine tuning the descriptions of the previously identified priorities.
“In the previous regulatory science prioritizations at CDRH (FY2016
), a call for regulatory science needs was made to center staff. Upon comparing the final priority lists generated from those calls, very little changed to the overarching categories/themes of the priorities, and many of the priorities aligned with the center’s 2018-2022 strategic plan
,” CDRH writes.
While the priorities may be the same, the new report goes into greater detail and now lists sets of objectives for each area, whereas the previous two reports merely provided descriptions of each topic.
For instance, in the FY2017 report, the section on computational modeling discusses use cases and areas of opportunity for computation modeling, while the new report also sets out four objectives for the agency to focus on. Those objectives include establishing infrastructure for tracking the use of simulation in regulatory submissions; focusing computational modeling on device areas where experimental data is difficult to obtain; developing benchmarks and end-to-end examples using mock submissions; and establishing appraisal metrics for companies to certify their simulation practices.