Scale‐up of a monoclonal antibody <scp>CHO</scp> fed‐batch production in stirred tank bioreactors: Effect of hydrodynamic conditions and feeding regimen
Bibliographic record
Abstract
Key hydrodynamic-related parameters such as volumetric power input (P/V), impeller configuration, aeration strategy, and maximum gas sparge rate, as well as an appropriate feeding strategy, must be carefully selected to improve production yields in bioreactor. In this study, the feeding regimen was found to have an important impact on cell growth and productivity of a cumate-inducible CHO fed-batch cell culture. A low-volume feeding regimen avoided a rapid increase in osmolality, allowing for prolonged cell viability and a 33% increase in volumetric titer compared to the high-volume feeding regimen. Both sparged air and oxygen were used for dissolved oxygen (DO) control, utilizing three levels of airflow rates. An optimum airflow rate of 0.0031 vvm was found to improve cell growth, longevity, and thus final titer. A larger air cap required increased gas flow rates, which led to an earlier cell mortality. Scale-up from 1-L to 10-L bioreactor using constant P/V and air cap volumetric gas flow rate (vvm) allowed for comparable cell growth and productivity. Further investigation of the effect of mixing and aeration was done by maintaining P/V and vvm constant throughout the cell culture, which further improved product titers at 11 days after induction. Our study also demonstrates that keeping a constant volume by removing a culture amount equal to the feed volume added at each sampling event can significantly improve the final volumetric titer. This finding shows the benefit of developing a concentrated feed to reduce the volume increase, which in turn could greatly ease the scale-up task.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".