Quasi-Steady-State Approach for Efficient Multiscale Simulation and Optimization of mAb Glycosylation in CHO Cell Culture

TL;DR

This paper introduces a quasi-steady-state (QSS) method that significantly accelerates multiscale glycosylation modeling in mAb production, enabling efficient optimization and control with minimal accuracy loss.

Contribution

The authors develop a novel QSS approach that simplifies complex multiscale models, reducing computational time by over 300 times while maintaining high accuracy.

Findings

QSS method is over 300 times faster than traditional methods.

Relative error of the QSS approach is less than 1.6%.

Enables efficient model-based optimization and control of glycosylation.

Abstract

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.