The use of Raman models for glucose and phenylalanine concentrations to provide the signal for a control algorithm to continuously adjust the feed rate of two separate supplemental feeds during the fed-batch culture of a CHOK1SV GS-KO® cell line in a platform process was evaluated. Automated feed rate adjustment of the glucose feed using a Raman model for glucose concentration, maintained the glucose concentration within the desired target (average deviation ± 0.49 g/L). Automated feed rate adjustment of the nutrient feed using a Raman model for phenylalanine concentration, maintained phenylalanine concentrations within the target (average deviation ± 29.97 mg/L). The novel use of a Raman model for phenylalanine concentration, combined with a Raman model for glucose concentration, to maintain target glucose and phenylalanine concentrations through feed-rate adjustments, reduced the average cumulative glucose and nutrient feed additions (19% and 27% respectively) compared to manually adjusted cultures. Additionally, the proposed automation strategy led to lower osmolality during culture, maintained the nutrient environment more consistently, and achieved higher harvest product concentration (≈ 20% higher) compared to typical fed-batch process control for the cell line and platform process evaluated. Furthermore, the proposed feeding strategy yielded similar glycosylation and charge variant profiles compared to manually adjusted fed-batch process control. The ability to continuously adjust the feed rate addition of two separate feeds in this manner helps enable a shift away from the current daily offline sampling needed to control fed-batch mammalian cell culture during clinical and commercial manufacturing on platform processes.

评估了使用葡萄糖和苯丙氨酸浓度的拉曼模型为控制算法提供信号，以在平台过程中 CHOK1SV GS-KO® 细胞系的补料分批培养期间连续调整两种单独补充补料的补料速率. 使用用于葡萄糖浓度的拉曼模型对葡萄糖进料进行自动进料速率调整，将葡萄糖浓度保持在所需的目标范围内（平均偏差 ± 0.49 g/L）。使用苯丙氨酸浓度拉曼模型自动调整营养饲料的进料速率，将苯丙氨酸浓度保持在目标范围内（平均偏差 ± 29.97 mg/L）。苯丙氨酸浓度拉曼模型的新用途，结合葡萄糖浓度的拉曼模型，为了通过调整进料速率来维持目标葡萄糖和苯丙氨酸浓度，与手动调整的培养物相比，减少了平均累积葡萄糖和营养饲料添加量（分别为 19% 和 27%）。此外，与用于评估的细胞系和平台过程的典型补料分批过程控制相比，所提出的自动化策略导致培养过程中的渗透压更低，更一致地维持营养环境，并实现更高的收获产品浓度（约高 20%）。此外，与手动调整的分批补料过程控制相比，所提出的补料策略产生了相似的糖基化和电荷变异特征。