Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary.
Department of Applied Biotechnology and Food Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary.
Int J Pharm. 2024 Jul 20;660:124251. doi: 10.1016/j.ijpharm.2024.124251. Epub 2024 May 24.
This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.
本研究详细比较了拉曼和近红外(NIR)光谱作为过程分析技术工具,用于实时监测蛋白质纯化过程。对拉曼和 NIR 光谱的应用和模型开发进行了全面调查,以实时监测 SARS-CoV-2 刺突蛋白受体结合域(RBD)切向流过滤过程中的相关杂质咪唑。使用离线校准数据快速开发了基于拉曼和 NIR 光谱的校准模型,导致校准和交叉验证误差较低。拉曼模型的 RMSEC 为 1.53mM,RMSECV 为 1.78mM,NIR 模型的 RMSEC 为 1.87mM,RMSECV 为 2.97mM。此外,当应用于在线测量系统时,拉曼模型具有良好的稳健性,但相反,NIR 光谱对测量环境的变化很敏感。通过利用所开发的模型,在线拉曼和 NIR 光谱成功应用于重组蛋白膜过滤过程中相关杂质的实时监测。结果增强了在诊断和治疗性蛋白质纯化更广泛领域实施实时监测方法的重要性,并强调了其在快速开发生物产品方面的潜力。