Glycosylation is a critical quality attribute for monoclonal antibody (mAb) production, influenced by both process conditions and cellular mechanisms. Multiscale mechanistic models, spanning from the bioreactor to the Golgi apparatus, have been proposed for analyzing the glycosylation process. However, these models are computationally intensive to solve when using traditional methods, making optimization and control challenging. In this work, we propose a quasi-steady-state (QSS) approach for efficiently solving the multiscale glycosylation model. By introducing the QSS assumption and assuming negligible nucleotide sugar donor (NSD) flux for glycosylation in the Golgi, the large-scale partial differential algebraic equation system is converted into a series of independent differential algebraic equation systems. Based on that representation, we develop a three-step QSS simulation method and further reduce computational time through parallel computing and nonuniform time grid strategies. Case studies in simulation, parameter estimation, and dynamic optimization demonstrate that the QSS approach can be more than 300-fold faster than the method of lines, with less than 1.6% relative errors. This work establishes a solid foundation for multiscale model-based optimization and control of the glycosylation process, supporting the implementation of quality by design.

翻译：糖基化是单克隆抗体生产中的关键质量属性，受工艺条件和细胞机制共同影响。已有研究提出从生物反应器到高尔基体的多尺度机理模型，用于分析糖基化过程。然而，使用传统方法求解这些模型计算量极大，使得优化与控制面临挑战。本研究提出一种准稳态方法，用于高效求解多尺度糖基化模型。通过引入准稳态假设，并假定高尔基体中用于糖基化的核苷酸糖供体通量可忽略，大规模偏微分代数方程组被转化为一系列独立的微分代数方程组。基于此表示形式，我们开发了一种三步准稳态模拟方法，并进一步通过并行计算与非均匀时间网格策略减少计算时间。在模拟、参数估计与动态优化中的案例研究表明，准稳态方法相比直线法可提速300倍以上，且相对误差小于1.6%。本工作为基于多尺度模型的糖基化过程优化与控制奠定了坚实基础，支持质量源于设计理念的实施。