Fed‐batch strategies for intensified <scp>rVSV</scp> vector production in high cell density cultures of suspension <scp>HEK293</scp> cells
Bibliographic record
Abstract
Abstract Vesicular stomatitis virus (VSV) has been increasingly demonstrated as a promising viral vector platform. As the interest over this modality for vaccine and gene therapy applications increases, the need for intensified processes to produce these vectors emerge. In this study, we develop fed‐batch‐based operations to intensify the production of a recombinant VSV‐based vaccine candidate (rVSV‐SARS‐CoV‐2) in suspension cultures of HEK293 cells. A feeding strategy, in which a commercial concentrated medium was added to cultures based on cell growth through a fixed cell specific feeding rate (CSFR), was applied for the development of two different processes using Ambr250 modular bioreactors. Cultures operated in hybrid fed‐batch/perfusion (FB/P) or fed‐batch (FB) were able to sustain infections performed at 8.0 × 10 6 cells/mL, respectively resulting in 3.9 and 5.0‐fold increase in total yield ( Y T ) and 1.7 and 5.6‐fold increase in volumetric productivity (VP) when compared with a batch reference. A maximum viral titer of 4.5 × 10 10 TCID 50 /mL was reached, which is comparable or higher than other processes for VSV production in different cell lines. Overall, our study reports efficient fed‐batch options to intensify the production of a rVSV‐based vaccine candidate in suspension HEK293 cells.
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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 | 0.000 |
| 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) | 0.001 | 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".