Institute for Pharma Technology, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.
Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria.
Biotechnol J. 2022 Nov;17(11):e2200184. doi: 10.1002/biot.202200184. Epub 2022 Aug 7.
Raman spectroscopy has gained popularity to monitor multiple process indicators simultaneously in biopharmaceutical processes. However, robust and specific model calibration remains a challenge due to insufficient analyte variability to train the models and high cross-correlation of various media components and artifacts throughout the process.
A systematic Raman calibration workflow for perfusion processes enabling highly specific and fast model calibration was developed. Harvest libraries consisting of frozen harvest samples from multiple CHO cell culture bioreactors collected at different process times were established. Model calibration was subsequently performed in an offline setup using a flow cell by spiking process harvest with glucose, raffinose, galactose, mannose, and fructose.
In a screening phase, Raman spectroscopy was proven capable not only to distinguish sugars with similar chemical structures in perfusion harvest but also to quantify them independently in process-relevant concentrations. In a second phase, a robust and highly specific calibration model for simultaneous glucose (root mean square error prediction [RMSEP] = 0.32 g L ) and raffinose (RMSEP = 0.17 g L ) real-time monitoring was generated and verified in a third phase during a perfusion process.
The proposed novel offline calibration workflow allowed proper Raman peak decoupling, reduced calibration time from months down to days, and can be applied to other analytes of interest including lactate, ammonia, amino acids, or product titer.
拉曼光谱已广泛用于同时监测生物制药过程中的多个过程指标。然而，由于模型训练所需的分析物变化性不足，以及过程中各种介质成分和干扰物之间存在高度的交叉相关性，因此稳健且特定的模型校准仍然是一个挑战。
开发了一种用于灌注过程的系统拉曼校准工作流程，可实现高度特定和快速的模型校准。建立了由来自多个 CHO 细胞培养生物反应器的冷冻收获样本组成的收获库，这些样本是在不同的过程时间收集的。随后，通过在离线设置中使用流量池，向过程收获物中添加葡萄糖、棉子糖、半乳糖、甘露糖和果糖，进行了模型校准。
在筛选阶段，拉曼光谱不仅能够区分灌注收获物中具有相似化学结构的糖，而且还能够在相关过程浓度下独立定量它们。在第二阶段，生成了一种用于实时监测葡萄糖（均方根误差预测 [RMSEP] = 0.32 g/L）和棉子糖（RMSEP = 0.17 g/L）的稳健且高度特定的校准模型，并在第三阶段的灌注过程中进行了验证。
所提出的新型离线校准工作流程允许适当的拉曼峰解耦，将校准时间从数月缩短到数天，并且可以应用于其他感兴趣的分析物，包括乳酸、氨、氨基酸或产物滴度。
