AI-enabled lyophilization of advanced biologics: process intelligence, quality assessment, and formulation...

Lyophilization is a key stabilization strategy for advanced biologics, including protein therapeutics, nucleic-acid-based products, extracellular vesicles, and other highly labile biopharmaceutical systems. However, the development of lyophilized biologic products remains constrained by the intrinsic complexity of coupled heat and mass transfer, limited real-time process observability, formulation-dependent instability, and the difficulty of linking process parameters to product-relevant critical quality attributes. In this context, artificial intelligence (AI) is emerging not merely as an auxiliary optimization tool, but as an enabling approach for data-driven process understanding, predictive quality assessment, and rational formulation-process development. This review focuses on recent progress and emerging opportunities at the intersection of AI-enabled lyophilization and advanced biologic preservation, with particular emphasis on process intelligence and formulation intelligence. We discuss how machine learning, computer vision, spectroscopy-integrated analytics, hybrid modeling, and digital twins are being explored to improve process modeling, state estimation, cycle optimization, defect detection, and critical quality attribute prediction, while highlighting formulation design as a strategically important but still early-stage frontier. We further propose that future progress in lyophilization of advanced biologics will increasingly rely on integrated frameworks that connect mechanistic knowledge, multimodal characterization, AI-assisted decision support, and product-relevant validation across both process development and formulation design.