Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Abstract

Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

Modelling Human Locomotion to Inform Exercise Prescription for Osteoporosis

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Precision Medicine in Interventional Cardiology

“…The rapid development of computational modelling may provide new possibilities to predict the risk of developing certain diseases, test new therapeutic treatments, improve medical device safety and select the best therapeutic options for individual patients. 6 In silico testing, where computational modelling and artificial intelligence are combined, is an interesting new development. The Virtual Physiological Human (VPH) project is an example of this concept and started in 2005 with three objectives: introduce patient-specific modelling to support medical decision; apply in silico clinical trials to test new treatments and duplicate the robustness of clinical trials with large samples in a virtual clinical trial (its main objective); and introduce patient-specific, real-time simulations to devise tailored treatment for the individual patient.…”

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“…Simulation models are well-established in engineering sciences and applied in the design and implementation of complex, man-made systems. Computer simulation becomes increasingly important in biomedical research and supports regulatory processes for new medical devices and drugs [4], [5]. However, there is remarkably little attention for a systematic approach to incorporate differences in in vivo physiology, disease development and treatment response between humans and animals in the computer simulation models.…”