Device-Embedded Drug Stability Studies

Abstract

Drug stability studies for device-embedded therapeutic agents differ fundamentally from stability studies for conventional pharmaceutical dosage forms. A drug formulated within or coated onto a medical device is exposed to physical, chemical, and thermal stresses across the combination product's manufacturing, storage, deployment, and in vivo operational lifetimes -- stresses that include polymer-drug interactions during sterilisation, temperature and humidity variation throughout multi-year implanted operation, mechanical strain during catheter deployment, and sustained aqueous exposure following implantation. The ICH Q1A(R2) stability guideline that anchors pharmaceutical stability study design was developed for solid and liquid dosage forms in defined container closure systems, and its extrapolation to device-embedded drug products requires substantial adaptation. This study presents the Device-Embedded Drug Stability Assessment Framework (DEDSAF), evaluating five stability study design approaches -- traditional ICH Q1A(R2)-aligned stability studies (ICH-TS), accelerated stability modelling with mechanistic prediction (ASM-MP), device-simulation stability testing incorporating use condition stresses (DSS-UC), real-time in vivo stability monitoring with explant analysis (RTV-EA), and AI-enhanced stability prediction integrating multi-stress data (AI-SP) -- across four device-embedded drug contexts: drug-eluting coronary stents, implantable drug-delivery pumps with extended reservoir storage, drug-coated orthopedic implants, and long-acting injectable depot formulations deployed through device applicators. Performance was scored using the Stability Study Effectiveness Score (SSES), a weighted composite of in-use stability prediction accuracy (0.30), regulatory acceptance (0.20), study duration efficiency (0.20), device-specific stress characterisation (0.15), and operational feasibility (0.15). AI-enhanced stability prediction achieved the highest SSES (0.886) through strong in-use prediction accuracy (0.940) and study duration efficiency (0.920). Device-simulation stability testing ranked second (0.877) with the best device-specific stress characterisation (0.920). Real-time in vivo stability monitoring (0.864) provides the gold standard for prediction validation. Accelerated stability modelling (0.856) supports extrapolation from shorter studies. Traditional ICH-aligned stability studies (0.829) maintain the highest regulatory acceptance but show limitations for device-specific stress profiles