Cell culture processes for monoclonal antibody production
Open accessAbstract
Animal cell culture technology has advanced significantly over the last few decades and is now generally considered a reliable, robust and relatively mature technology. A range of biotherapeutics are currently synthesized using cell culture methods in large scale manufacturing facilities that produce products for both commercial use and clinical studies. The robust implementation of this technology requires optimization of a number of variables, including 1) cell lines capable of synthesizing the required molecules at high productivities that ensure low operating cost; 2) culture media and bioreactor culture conditions that achieve both the requisite productivity and meet product quality specifications; 3) appropriate on-line and off-line sensors capable of providing information that enhances process knowledge; and 4) good understanding of culture performance at different scales to ensure smooth scale-up. Successful implementation also requires appropriate strategies for process development, scale-up and process characterization and validation that enable robust operation that is compliant with current regulations. This review provides an overview of the state-of-the art technology in key aspects of cell culture, e.g., engineering of highly productive cell lines and optimization of cell culture process conditions. We also summarize the current thinking on appropriate process development strategies and process advances that might affect process development.
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The stability of GIx might be explained by narrow operation windows for process interventions (e.g., pH ~6.7-7.4; T 30-38°C).
“The first is associated with the narrow operation windows for process interventions (e.g., pH ~ 6.7–7.4; T 30–38 °C).71”
From Impact of process parameters on IgG glycosylation in CHO systems: a comprehensive quantitative analysis (2026) · cites this paper as [71]
Differences in culture systems, including bioreactor configuration, process control strategies, sensor technologies, and process parameter measurement approaches, can introduce variability.
“differences in culture systems (including bioreactor configuration, process control strategies, sensor technologies, and PP measurement approaches) can introduce variability.71”
From Impact of process parameters on IgG glycosylation in CHO systems: a comprehensive quantitative analysis (2026) · cites this paper as [71]
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- Authors
- Li, F., Vijayasankaran, N., Shen, A. (., Kiss, R., & Amanullah, A.
- Affiliations
- Genentech (US)
- Corresponding author
- Amanullah, Ashraf
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2010-09
- Volume
- 2
- Issue
- 5
- Pages
- 466-479
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC2958569
- OpenAlex
- W2168338758
- Cited by
- 739
- References
- 88
- Citations by year
- 2026: 34 · 2025: 38 · 2024: 66 · 2023: 50 · 2022: 42 · 2021: 64 · 2020: 53 · 2019: 55 · 2018: 79 · 2017: 66 · 2016: 44 · 2015: 45 · 2014: 38 · 2013: 38 · 2012: 19
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- mAbs (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Viral Infectious Diseases and Gene Expression in Insects
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Keywords
- monoclonal antibody production, animal cell culture, bioreactors, cell line engineering, culture medium optimization, bioprocess scale-up, process development, process validation, regulatory compliance
- MeSH terms
- Animals, Antibodies, Monoclonal, Cell Line, Transformed, Cricetulus, Cricetinae, Humans, Technology, Pharmaceutical, CHO Cells, Cell Culture Techniques, Cell Line, Tumor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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