Advanced Biocompatibility Testing Models

Abstract

Biocompatibility testing for medical devices has been anchored by the ISO 10993 standard series since its first publication in 1992, establishing a risk-based battery of in vitro and in vivo tests that has served as the regulatory cornerstone for device-material safety assessment globally. Over the same period, advances in three-dimensional cell culture, organ-on-chip microfabrication, computational chemistry, and multi-omics profiling have produced testing models that more faithfully replicate human tissue responses to implant materials than the conventional in vitro methods -- while also reducing the animal study requirements that the 3Rs framework has been systematically pressing regulators to minimise. The alignment between regulatory pressure toward animal replacement and the technical maturity of advanced testing models creates a strategic inflection point for device biocompatibility science. This study presents the Advanced Biocompatibility Testing Assessment Framework (ABTAF), evaluating five testing model types -- traditional ISO 10993 in vitro battery (ISO-IVB), 3D tissue model testing (3DTM), in silico computational biocompatibility modelling (InSil-CB), microphysiological organ-on-chip systems (MPS-OC), and AI-enhanced multi-omics biocompatibility profiling (AI-MOP) - across four device material contexts: metallic implant alloys, polymer-based implants and coatings, bioactive ceramics and glasses, and novel nanomaterials with surface modifications. Performance was scored using the Biocompatibility Testing Effectiveness Score (BTES), a weighted composite of predictive accuracy (0.30), animal replacement potential (0.20), regulatory acceptance (0.20), implementation complexity-inverse (0.15), and throughput scalability (0.15). In silico computational modelling achieved the highest BTES (0.855) through the best animal replacement potential (0.960) and strong throughput scalability (0.840), despite lower predictive accuracy than microphysiological systems. AI-enhanced multi-omics profiling ranked second (0.848) with strong predictive accuracy (0.900). Microphysiological systems scored third (0.838) with the best predictive accuracy (0.920) but were constrained by implementation complexity. Traditional ISO testing ranked lowest (0.816) due to the lowest animal replacement potential (0.600) -- the dimension increasingly weighted in regulatory and ethical frameworks.