Bioreactor production of rVSV‐based vectors in Vero cell suspension cultures
Bibliographic record
Abstract
Abstract The Vero cell line is the most used continuous cell line in viral vaccine manufacturing. This adherent cell culture platform requires the use of surfaces to support cell growth, typically roller bottles, or microcarriers. We have recently compared the production of rVSV‐ZEBOV on Vero cells between microcarrier and fixed‐bed bioreactors. However, suspension cultures are considered superior with regard to process scalability. Therefore, we further explore the Vero suspension system for recombinant vesicular stomatitis virus (rVSV)‐vectored vaccine production. Previously, this suspension cell line was only able to be cultivated in a proprietary medium. Here, we expand the adaptation and bioreactor cultivation to a serum‐free commercial medium. Following small‐scale optimization and screening studies, we demonstrate bioreactor productions of highly relevant vaccines and vaccine candidates against Ebola virus disease, HIV, and coronavirus disease 2019 in the Vero suspension system. rVSV‐ZEBOV, rVSV‐HIV, and rVSVInd‐msp‐SF‐Gtc can replicate to high titers in the bioreactor, reaching 3.87 × 107 TCID50/ml, 2.12 × 107 TCID50/ml, and 3.59 × 109 TCID50/ml, respectively. Furthermore, we compare cell‐specific productivities, and the quality of the produced viruses by determining the ratio of total viral particles to infectious viral particles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".