Fed‐batch CHO cell t‐PA production and feed glutamine replacement to reduce ammonia production
Bibliographic record
Abstract
Industrial therapeutic protein production has been greatly improved through fed-batch development. In this study, improvement to the productivity of a tissue-plasminogen activator (t-PA) expressing Chinese hamster ovary (CHO) cell line was investigated in shake flask culture through the optimization of the fed-batch feed and the reduction of ammonia generation by glutamine replacement. The t-PA titer was increased from 33 mg/L under batch conditions to 250 mg/L with daily feeding starting after three days of culture. A commercially available fed-batch feed was supplemented with cotton seed hydrolysate and the four depleted amino acids, aspartic acid, asparagine, cysteine, and tyrosine. The fed-batch operation increased the generation of by-products such as lactate and ammonia that can adversely affect the fed-batch performance. To reduce the ammonia production, a glutamine-containing dipeptide, pyruvate, glutamate, and wheat gluten hydrolysate, were investigated as glutamine substitutes. To minimize the lag phase as the cells adjusted to the new energy source, a feed glutamine replacement process was developed where the cells were initially cultured with a glutamine containing basal medium to establish cell growth followed by feeding with a feed containing the glutamine substitutes. This two-step feed glutamine replacement process not only reduced the ammonia levels by over 45% but, in the case of using wheat gluten hydrolysate, almost doubled the t-PA titer to over 420 mg/L without compromising the t-PA product quality or glycosylation pattern. The feed glutamine replacement process combined with optimizing other feed medium components provided a simple, practical, and effective fed-batch strategy that could be applied to the production of other recombinant therapeutic proteins.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".