Repressing expression of difficult‐to‐express recombinant proteins during the selection process increases productivity of CHO stable pools
Bibliographic record
Abstract
More than half of licensed therapeutic recombinant proteins (r-proteins) are manufactured using constitutively-expressing, stably-transfected Chinese hamster ovary (CHO) clones. While constitutive CHO expression systems have proven their efficacy for the manufacturing of monoclonal antibodies, many next-generation therapeutics such as cytokines and bispecific antibodies as well as biological targets such as ectodomains of transmembrane receptors remain intrinsically challenging to produce. Herein, we exploited a cumate-inducible CHO platform allowing reduced expression of various classes of r-proteins during selection of stable pools. Following stable pool generation, fed-batch productions showed that pools generated without cumate (OFF-pools) were significantly more productive than pools selected in the presence of cumate (ON-pools) for 8 out of the 10 r-proteins tested, including cytokines, G-protein coupled receptors (GPCRs), the HVEM membrane receptor ectodomain, the multifunctional protein High Mobility Group protein B1 (HMGB1), as well as monoclonal and bispecific T-cell engager antibodies. We showed that OFF-pools contain a significantly larger proportion of cells producing high levels of r-proteins and that these cells tend to proliferate faster when expression is turned off, suggesting that r-protein overexpression imposes a metabolic burden on the cells. Cell viability was lower and pool recovery was delayed during selection of ON-pools (mimicking constitutive expression), suggesting that high producers were likely lost or overgrown by faster-growing, low-producing cells. We also observed a correlation between the expression levels of the GPCRs with Binding immunoglobulin Protein, an endoplasmic reticulum (ER) stress marker. Taken together, these data suggest that using an inducible system to minimize r-protein expression during stable CHO pool selection reduces cellular stresses, including ER stress and metabolic burden, leading to pools with greater frequency of high-expressing cells, resulting in improved volumetric productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".