Computer-Aided Strategies for Determining the Amino Acid Composition of Medium for Chinese Hamster Ovary Cell-Based Biomanufacturing Platforms

Abstract

Chinese hamster ovary (CHO) cells are used for the production of the majority of biopharmaceutical drugs, and thus have remained the standard industry host for the past three decades. The amino acid composition of the medium plays a key role in commercial scale biologics manufacturing, as amino acids constitute the building blocks of both endogenous and heterologous proteins, are involved in metabolic and non-metabolic pathways, and can act as main sources of nitrogen and carbon under certain conditions. As biomanufactured proteins become increasingly complex, the adoption of model-based approaches become ever more popular in complementing the challenging task of medium development. The extensively studied amino acid metabolism is exceptionally suitable for such model-driven analyses, and although still limited in practice, the development of these strategies is gaining attention, particularly in this domain. This paper provides a review of recent efforts. We first provide an overview of the widely adopted practice, and move on to describe the model-driven approaches employed for the improvement and optimization of the external amino acid supply in light of cellular amino acid demand. We conclude by proposing the likely prevalent direction the field is heading towards, providing a critical evaluation of the current state and the future challenges and considerations.

A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures

“…At the foundation of our method is a genome-scale metabolic network model, which accounts for the complex conversion from media nutrients to biomass and recombinant protein production. Such models have been increasingly utilized for CHO cells (Hefzi et al, 2016;Calmels et al, 2019;Huang & Yoon, 2020) and bioprocess applications (Sommeregger et al, 2017;Zhang & Hua, 2016), such as predicting clonal performances (Popp et al, 2016), identifying metabolic bottlenecks (Zhuangrong & Seongkyu, 2020), and optimizing media formulation (Fouladiha et al, 2020;Traustason et al, 2019). Metabolic network models can also estimate amino acid uptake rates necessary to experimentally support observed proliferation and productivity (Chen et al, 2019).…”

A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures

“…At the foundation of our method is a genome-scale metabolic network model, which accounts for the complex conversion from media nutrients to biomass and recombinant protein production. Such models have been increasingly utilized for CHO cells (Hefzi et al, 2016;Calmels et al, 2019;Huang & Yoon, 2020) and bioprocess applications (Sommeregger et al, 2017), such as predicting clonal performances (Popp et al, 2016), identifying metabolic bottlenecks (Zhuangrong & Seongkyu, 2020), and optimizing media formulation (Fouladiha et al, 2020;Traustason et al, 2019). Metabolic network models can also estimate amino acid 4 uptake rates necessary to experimentally support observed proliferation and productivity (Chen et al, 2019).…”

Establishment of fast-growing serum-free immortalised cells from Chinese hamster lung tissues for biopharmaceutical production

“…CHO cells are widely used in the biopharmaceutical industry to produce recombinant proteins such as immunoglobulin G (IgG) 2 . One of the characteristics of CHO cells is that they can adapt to serum-free medium and can be cultured in chemically defined medium without animal-derived components 3 – 5 . When CHO cells are cultured in serum-free medium, the cells detach from the surface and float, which enables large-scale high density cell cultures in bioreactors.…”