Arndt Lukas, Wiegmann Vincent, Kuchemüller Kim B, Baganz Frank, Pörtner Ralf, Möller Johannes
Hamburg University of Technology, Bioprocess and Biosystems Engineering, Hamburg, Germany.
University College London, The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, London, UK.
Biotechnol Prog. 2021 May;37(3):e3122. doi: 10.1002/btpr.3122. Epub 2021 Jan 27.
Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR- to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scale-up. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences. In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.
小型生物反应器（MBR）系统常用于哺乳动物细胞培养工艺的开发。然而，由于主要采用非整体放大方法，将在MBR中获得的工艺策略放大到更大规模具有挑战性。在本研究中，引入了一种基于模型的工作流程，以量化生物反应器规模之间的过程动力学差异，从而实现更具知识驱动性的放大。该工作流程应用于两个使用产抗体中国仓鼠卵巢细胞系的案例研究。通过该工作流程，首先在考虑不同规模实验变异性的情况下估计模型参数分布。其次，对获得的单个模型参数分布进行统计差异检验。在存在显著差异的情况下，在不同规模之间转移模型参数分布。在案例研究I中，对微量滴定板（工作体积4毫升）和实验室规模生物反应器（工作体积3750毫升）中的补料分批培养过程进行了数学建模和评估。未发现反映过程动力学的模型参数分布存在显著差异。因此，微量滴定板可作为实验室规模生物反应器的缩小模型工具。在案例研究II中，研究了在24孔深孔板（工作体积2毫升）和摇瓶（工作体积40毫升）中使用两种进料培养基的补料分批培养过程。模型参数分布显示出显著差异。因此，对工艺策略进行了数学转移，并对新的摇瓶培养设置模拟了模型预测，并在验证实验中得到了证实。总体而言，该工作流程能够对放大进行知识驱动的评估，以实现更高效的生物工艺设计和优化。
