The generation of stable, high MAb expressing CHO cell lines based on the artificial chromosome expression (ACE) technology
Bibliographic record
Abstract
The manufacture of recombinant proteins at industrially relevant levels requires technologies that can engineer stable, high expressing cell lines rapidly, reproducibly and with relative ease. Commonly used methods incorporate transfection of mammalian cell lines with plasmid DNA containing the gene of interest. Identifying stable high expressing transfectants is normally laborious and time consuming. To improve this process, the ACE System has been developed based on pre-engineered artificial chromosomes with multiple recombination acceptor sites. This system allows for the targeted transfection of single or multiple genes and eliminates the need for random integration into native host chromosomes. To illustrate the utility of the ACE System in generating stable, high expressing cell lines, CHO based candidate cell lines were generated to express a human monoclonal IgG1 antibody. Candidate cell lines were generated in under 6 months and expressed over 1 g/L and with specific productivities of up to 45 pg/cell/day under non-fed, non-optimized shake flask conditions. These candidate cell lines were shown to have stable expression of the monoclonal antibody for up to 70 days of continuous culture. The results of this study demonstrate that clonal, stable monoclonal antibody expressing CHO based cell lines can be generated by the ACE System rapidly and perform competitively with those cell lines generated by existing technologies. The ACE System, therefore, provides an attractive and practical alternative to conventional methods of cell line generation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".