Advanced glucose control strategies leveraging Raman spectroscopy for optimized mammalian cell culture manufacturing

Abstract Maintaining consistent quality in the manufacturing of biotherapeutic proteins in mammalian cell culture is challenging, with unplanned deviations causing inconsistencies and potential batch failure. Current methods for monitoring and controlling critical process parameters (CPPs) rely on slow, labor‐intensive offline analyses. This is particularly problematic in upstream manufacturing, where infrequent measurements hinder real‐time CPP monitoring and result in suboptimal control. This study developed and implemented a robust, standardized framework that integrates Raman spectroscopy with real‐time machine learning models and bioreactor control via OPC‐UA, enabling seamless communication and effective control of mammalian cell culture CPPs. High‐accuracy ML models ( R 2 >0.92) enabled real‐time monitoring of glucose, lactate, viable cell density, and antibody titre. The glucose model was integrated into an automated process analytical technology (PAT) control strategy to maintain glucose at a predefined setpoint. The PAT strategy was compared to a manual bolus glucose control under high and low glucose feeding regimes. The PAT control approach observed an increase in biotherapeutic product titre by up to 35% and reduced glycation by up to 27%. These results indicated that both glucose setpoints and fluctuations impacted cell culture performance. In conclusion, this study highlighted the value of PAT tools in automating control loops, consequently improving process performance with enhanced productivity and quality.