Process development for an inducible rituximab‐expressing Chinese hamster ovary cell line
Bibliographic record
Abstract
Inducible mammalian expression systems are becoming increasingly available and are not only useful for the production of cytotoxic/cytostatic products, but also confer the unique ability to uncouple the growth and production phases. In this work, we have specifically investigated how the cell culture state at the time of induction influences the cumate‐inducible expression of recombinant rituximab by a GS‐CHO cell line. To this end, cells grown in batch and fed‐batch cultures were induced at increasing cell densities (1 to 10 × 10 6 cells/mL). In batch, the cell specific productivity and the product yield were found to reduce with increasing cell density at induction. A dynamic feeding strategy using a concentrated nutrient solution applied prior and postinduction allowed to significantly increase the integral of viable cells and led to a 3‐fold increase in the volumetric productivity (1.2 g/L). The highest product yields were achieved for intermediate cell densities at induction, as cultures induced during the late exponential phase (10 × 10 6 cells/mL) were associated with a shortened production phase. The final glycosylation patterns remained however similar, irrespective of the cell density at induction. The kinetics of growth and production in a 2 L bioreactor were largely comparable to shake flasks for a similar cell density at induction. The degree of galactosylation was found to decrease over time, but the final glycan distribution at harvest was consistent to that of the shake flasks cultures. Taken together, our results provide useful insights for the rational development of fed‐batch cell culture processes involving inducible CHO cells. © 2018 American Institute of Chemical Engineers Biotechnol. Prog ., 35: e2742, 2019
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".