<scp>CHO</scp> stable pool fed‐batch process development of <scp>SARS</scp> ‐ <scp>CoV</scp> ‐2 spike protein production: Impact of aeration conditions and feeding strategies
Bibliographic record
Abstract
Abstract Technology scale‐up and transfer are a fundamental and critical part of process development in biomanufacturing. Important bioreactor hydrodynamic characteristics such as working volume, overhead gas flow rate, volumetric power input (P/V), impeller type, agitation regimen, sparging aeration strategy, sparger type, and k L a must be selected based on key performance indicators (KPI) to ensure a smooth and seamless process scale‐up and transfer. Finding suitable operational setpoints and developing an efficient feeding regimen to ensure process efficacy and consistency are instrumental. In this investigation, process development of a cumate inducible Chinese hamster ovary (CHO) stable pool expressing trimeric SARS‐CoV‐2 spike protein in 1.8 L benchtop stirred‐tank bioreactors is detailed. Various dissolved oxygen levels and aeration air caps were studied to determine their impact on cell growth and metabolism, culture longevity, and endpoint product titers. Once hydrodynamic conditions were tuned to an optimal zone, various feeding strategies were explored to increase culture performance. Dynamic feedings such as feeding based on current culture volume, viable cell density (VCD), oxygen uptake rate (OUR), and bio‐capacitance signals were tested and compared to standard bolus addition. Increases in integral of viable cell concentration (IVCC) (1.25‐fold) and protein yield (2.52‐fold), as well as greater culture longevity (extension of 5 days) were observed in dynamic feeding strategies when compared to periodic bolus feeding. Our study emphasizes the benefits of designing feeding strategies around metabolically relevant signals such as OUR and bio‐capacitance signals.
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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) | 0.000 | 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) | 0.001 | 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".