Webster Thaddaeus A, Hadley Brian C, Hilliard William, Jaques Colin, Mason Carrie
Lonza Biologics Inc., 101 International Dr, Portsmouth, NH, 03801.
Lonza Biologics plc, 228 Bath road, Slough, SL14DX.
Biotechnol Prog. 2018 May;34(3):730-737. doi: 10.1002/btpr.2633. Epub 2018 May 10.
The monitoring and control of bioprocesses is of the utmost importance in order to provide a consistent, safe, and high-quality product for consumers. Current monitoring and control schemes rely on infrequent and time consuming offline sampling methods, which inherently leads to some variability in the process which may impact the product quality profile. As part of Lonza's dedication to process analytical technology (PAT) initiatives this study evaluated the ability to generate generic calibration models, which are independent of the cell line, using Raman probes to monitor changes in glucose, lactate, glutamate, ammonium, viable cell concentration (VCC), total cell concentration (TCC) and product concentration. Calibration models were developed from cell culture using two different CHOK1SV GS-KO cell lines producing different monoclonal antibodies (mAbs). Developed predictive models, measured changes in glucose, lactate, ammonium, VCC, and TCC with average prediction errors of 0.44, 0.23, 0.03 g L , 1.90 × 10 cells mL , and 1.85 × 10 cells mL , respectively over the course of cell culture with minimal cell line dependence. The development of these generic models allows the application of spectroscopic PAT techniques in clinical and commercial manufacturing environments, where processes are typically run once or twice in GMP manufacturing based on a common platform process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:730-737, 2018.
为了为消费者提供稳定、安全且高质量的产品,生物过程的监测与控制至关重要。当前的监测与控制方案依赖于不频繁且耗时的离线采样方法,这必然会导致过程中出现一些变异性,进而可能影响产品质量特征。作为龙沙集团致力于过程分析技术(PAT)计划的一部分,本研究评估了使用拉曼探针监测葡萄糖、乳酸、谷氨酸、铵、活细胞浓度(VCC)、总细胞浓度(TCC)和产品浓度变化时生成通用校准模型的能力,这些模型与细胞系无关。校准模型是通过使用两种不同的产生不同单克隆抗体(mAb)的CHOK1SV GS - KO细胞系进行细胞培养而开发的。所开发的预测模型在细胞培养过程中分别测量了葡萄糖、乳酸、铵、VCC和TCC的变化,平均预测误差分别为0.44、0.23、0.03 g/L、1.90×10⁶个细胞/mL和1.85×10⁶个细胞/mL,且对细胞系的依赖性最小。这些通用模型的开发使得光谱PAT技术能够应用于临床和商业制造环境,在这些环境中,基于通用平台工艺的过程通常在GMP制造中运行一到两次。© 2018美国化学工程师学会生物技术进展,34:730 - 737,2018。