AI-predictive vaccine stability: a systems biology framework to modernize regulatory testing and cold chain equity

Abstract

Vaccine instability contributes to the loss of up to 25 % of doses globally, a challenge intensified by the complexity of next-generation platforms such as mRNA–lipid nanoparticles (mRNA–LNPs), viral vectors, and protein subunits. Current regulatory frameworks (ICH Q5C, WHO TRS 1010) rely on static protocols that overlook platform-specific degradation mechanisms and real-world cold-chain variability. We introduce the Systems Biology–guided AI (SBg-AI) framework, a predictive stability platform integrating omics-derived biomarkers, real-time telemetry, and explainable machine learning. Leveraging recurrent and graph neural networks with Bayesian inference, SBg-AI forecasts degradation events with 89 % accuracy—validated in African and Southeast Asian supply chains. Federated learning ensures multi-manufacturer collaboration while preserving data privacy. In field trials, dynamic expiry predictions reduced mRNA vaccine wastage by 22 %. A phased regulatory roadmap supports transition from hybrid AI-empirical models (2024) to full AI-based stability determinations by 2030. By integrating mechanistic degradation science with real-time telemetry and regulatory-compliant AI, the SBg-AI framework transforms vaccine stability from retrospective batch testing to proactive, precision-guided assurance.