ArticlemAbs2026
Hybrid AI/ML-mechanistic framework enables intelligent optimization of commercial biopharmaceutical downstream processing.
Article in mAbs, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
15 authors.
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Abstract
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
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