Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective

TL;DR

This paper uses computational modeling to study how high-seeding-density processes affect metabolism in CHO cells for monoclonal antibody production.

Contribution

The study introduces a metabolic network and kinetic models to explore feeding strategies in high-seeding-density monoclonal antibody production.

Findings

High glycolytic fluxes and asparagine depletion were observed in high-seeding-density processes.

Adding a bolus medium with asparagine improved final monoclonal antibody titers.

Balancing asparagine concentration is crucial to prevent unwanted shifts in lactate metabolism.

Abstract

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.

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