Reliable scale-up of biopharmaceutical production processes is key in Quality by Design. In this study, a model-based workflow is described to evaluate the bioprocess dynamics during process transfer and scale-up computationally. First, a mathematical model describes the bioprocess dynamics of different state variables (e.g., cell density, titer). Second, the model parameter probability distributions are determined at different scales due to measurement uncertainty. Third, the quantified parameter distributions are statistically compared to evaluate if the process dynamics have been changed. This workflow was tested for the scale-up of an antibody-producing CHO fed-batch process. Significant differences were identified between the process development (30 ml) and implementation (250 ml) scale, and the feeding strategy was validated using model-assisted Design of Experiments. Then, the validated process strategy was successfully scaled up to 2 l laboratory and 50 l pilot scale. In summary, the proposed workflow enables a knowledge-driven evaluation tool for bioprocess development.

生物制药生产过程的可靠放大是“按质量设计”的关键。在这项研究中，描述了基于模型的工作流程，以评估过程转移和按比例放大计算过程中的生物过程动力学。首先，数学模型描述了不同状态变量（例如，细胞密度，滴度）的生物过程动力学。其次，由于测量不确定性，模型参数的概率分布是在不同的比例下确定的。第三，量化的参数分布在统计上进行比较，以评估过程动力学是否已更改。测试了此工作流程的规模，以扩大生产抗体的CHO补料分批过程的规模。在过程开发规模（30毫升）和实施规模（250毫升）之间发现了显着差异，并采用模型辅助实验设计验证了喂养策略。然后，将经过验证的过程策略成功扩展到2 l实验室和50 l中试规模。总之，提出的工作流程为生物工艺开发提供了一种知识驱动的评估工具。