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- W2983530548 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." @default.
- W2983530548 created "2019-11-22" @default.
- W2983530548 creator A5000409201 @default.
- W2983530548 creator A5005785706 @default.
- W2983530548 creator A5024762265 @default.
- W2983530548 date "2019-11-02" @default.
- W2983530548 modified "2023-09-24" @default.
- W2983530548 title "Computer-Aided Strategies for Determining the Amino Acid Composition of Medium for Chinese Hamster Ovary Cell-Based Biomanufacturing Platforms" @default.
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- W2983530548 doi "https://doi.org/10.3390/ijms20215464" @default.
- W2983530548 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6862603" @default.
- W2983530548 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31684012" @default.
- W2983530548 hasPublicationYear "2019" @default.
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