Regulatory Science in Biodevice Innovation

Abstract

Regulatory science -- the discipline of developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of regulated products -- has become the critical enabler for translating biodevice innovations from laboratory to clinical practice. As biodevices converge with AI algorithms, biological components, nanotechnology, and digital therapeutics, existing regulatory frameworks designed for mechanical implants and simple electronic devices face gaps that delay patient access to beneficial innovations while potentially admitting inadequately evaluated novel technologies. This study presents the Regulatory Science Assessment Framework for Biodevice Innovation (RSAFBI), evaluating five regulatory science approaches -- traditional predicate-based clearance pathways, performance-based standards development, regulatory sandbox programmes, total product lifecycle (TPLC) regulation, and computational modelling and simulation (CM&S;)-based evidence generation -- across four biodevice innovation categories: AI-enabled diagnostics, tissue-engineered implants, closed-loop therapeutic systems, and nano-enabled drug-device combinations. Our Regulatory Science Effectiveness Score (RSES) integrates pathway efficiency, evidence adequacy, innovation accommodation, post-market adaptability, and international harmonisation. Computational modelling and simulation achieved the highest RSES (0.924) through in silico clinical trials that generated regulatory-grade evidence for 68% of device performance questions at 12% of the cost and 20% of the timeline of physical clinical studies, while TPLC regulation achieved the highest post-market adaptability (0.958) through iterative evidence generation frameworks that continuously update the benefit-risk profile as real-world data accumulates.