Hybrid AI/ML-mechanistic framework enables intelligent optimization of commercial biopharmaceutical downstream...

Biopharmaceutical manufacturing requires continuous improvement to ensure robust, efficient, and high-quality processes, yet traditional experimental designs remain resource-demanding and insufficient to capture interactions of multiple parameters. Here, we introduce a hybrid framework integrating artificial intelligence (AI)/machine learning (ML) with mechanistic modeling to optimize anion-exchange chromatography and resolve the long-standing yield-purity trade-off in PEGylated protein purification. Three critical process parameters were first identified through correlation analysis between 30 input factors and critical quality attributes/process yield from 400+ commercial manufacturing lots, which were further refined using equilibrium dispersive and steric mass action models. Over 40,000 in silico optimization via the mechanistic model resolved the yield-purity trade-off, achieving a 12% increase in yield and 33% reduction in high-molecular-weight impurities. The optimized process conditions were verified across laboratory (n = 3), pilot (n = 3), and commercial (n = 18) runs, consistently demonstrating scalability and process robustness. This study highlights the power of combining data-driven machine learning with mechanistic modeling for process optimization, leading to an improved commercial process with substantial cost savings and paving the way for upcoming intelligent biomanufacturing.