A key aspect of large-scale production of biotherapeutics is a well-designed and consistently-executed upstream cell culture process. Process analytical technology tools provide enhanced monitoring and control capabilities to support consistent process execution, and also have potential to aid in maintenance of product quality at desired levels. One such tool, Raman spectroscopy, has matured as a useful technique to achieve real-time monitoring and control of key cell culture process attributes. We developed a Raman spectroscopy-based nutrient control strategy to enable dual control of lactate and glucose levels for a fed-batch CHO cell culture process for monoclonal antibody (mAb) production. To achieve this, partial least squares-based chemometric models for real-time prediction of glucose and lactate concentrations were developed and deployed in feedback control loops. In particular, feeding of lactic acid post-metabolic shift was investigated based on previous work that has shown the impact of lactate levels on ammonium as well as mAb product quality. Three feeding strategies were assessed for impact on cell metabolism, productivity, and product quality: bolus-fed glucose, glucose control at 4 g/L, or simultaneous glucose control at 4 g/L and lactate control at 2 g/L. The third feeding strategy resulted in a significant reduction in ammonium levels (68%) while increasing mAb galactosylation levels by approximately 50%. This work demonstrated that when deployed in a cell culture process, Raman spectroscopy is an effective technique for simultaneous control of multiple nutrient feeds, and that lactic acid feeding can have a positive impact on both cell metabolism and mAb product quality.

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Introduction The present article discusses the implementation of a semi-automated blend homogeneity control system by two near-infrared spectrometers. Methods A statistic was introduced to combine blend trends output by individual instruments based on the root mean squared error from the nominal value calculation. The necessity to monitor homogeneity at more than one location of a V-blender is highlighted and the impact of sensor and model differences on blend trends was evaluated. Using two different formulations, classical least-squares based models were developed to monitor blending. Calibration transfer between the two sensors was demonstrated as a useful approach when more than one sensor is used. Several classical transfer methods were implemented (optical, post-regression correction, and orthogonalization based) to balance the two sensors. Results and ConclusionResults showed that the use of only one calibration model, transferred to all units monitoring the process was highly beneficial to achieving consistent results. Specifically, standardization methods targeting instrument differences were demonstrated to be the most successful. However, results showed that the optimization of a given transfer method was formulation-dependent.