Rubini Morandise, Boyer Julien, Poulain Jordane, Berger Anaïs, Saillard Thomas, Louet Julien, Soucé Martin, Roussel Sylvie, Arnould Sylvain, Vergès Murielle, Chauchard-Rios Fabien, Chourpa Igor
Centre de Biophysique Moléculaire (UPR CNRS 4301), Département Nanomédicaments et Nanosondes, UFR de Pharmacie Philippe Maupas, Université de Tours, 31 avenue Monge, 37 000 Tours, France.
Ondalys, 4 Rue Georges Besse, 34 830 Clapiers, France.
Pharmaceutics. 2025 Apr 4;17(4):473. doi: 10.3390/pharmaceutics17040473.
Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy has emerged as a robust process analytical technology (PAT) tool due to its non-invasive, in situ capabilities. This study evaluates Raman spectroscopy for monitoring key metabolic parameters and IgG titer in CHO cell cultures. Raman spectroscopy was applied to five 10 L-scale CHO cell cultures. Partial least squares (PLS) regression models were developed from four batches, including one with induced cell death, to enhance robustness. The models were validated against blind test sets. PLS models exhibited high predictive accuracy (R > 0.9). Glucose and IgG titer predictions were reliable (RMSEP = 0.51 g/L and 0.12 g/L, respectively), while glutamine and lactate had higher RMSEP due to lower concentrations. Specific Raman bands contributed to the specificity of glucose, lactate, and IgG models. Predictions for viable (VCD) and total cell density (TCD) were less accurate due to the absence of direct Raman signals. This study confirms Raman spectroscopy's potential for real-time, in situ bioprocess monitoring without manual sampling. Chemometric analysis enhances model robustness, supporting automated control systems. Raman data could enable continuous feedback regulation of critical nutrients like glucose, ensuring consistent critical quality attributes (CQAs) in biopharmaceutical production.
中国仓鼠卵巢（CHO）细胞的代谢很复杂，受葡萄糖和谷氨酰胺等营养物质以及乳酸等代谢产物的影响。实时监测对于优化培养条件和确保产品质量的一致性至关重要。拉曼光谱由于其非侵入性、原位检测能力，已成为一种强大的过程分析技术（PAT）工具。本研究评估拉曼光谱用于监测CHO细胞培养中的关键代谢参数和IgG滴度。拉曼光谱应用于五个10升规模的CHO细胞培养物。从四批样本（包括一批诱导细胞死亡的样本）建立了偏最小二乘（PLS）回归模型，以提高稳健性。这些模型针对盲测集进行了验证。PLS模型表现出较高的预测准确性（R>0.9）。葡萄糖和IgG滴度的预测是可靠的（RMSEP分别为0.51 g/L和0.12 g/L），而谷氨酰胺和乳酸由于浓度较低，RMSEP较高。特定的拉曼谱带对葡萄糖、乳酸和IgG模型的特异性有贡献。由于缺乏直接的拉曼信号，对活细胞密度（VCD）和总细胞密度（TCD）的预测不太准确。本研究证实了拉曼光谱在无需手动采样的情况下进行实时、原位生物过程监测的潜力。化学计量分析提高了模型的稳健性，支持自动化控制系统。拉曼数据可以实现对葡萄糖等关键营养物质的连续反馈调节，确保生物制药生产中关键质量属性（CQA）的一致性。
