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.
Wang et al. (Wed,) studied this question.