High-titer manufacturing of SARS-CoV-2 Spike-pseudotyped VSV in stirred-tank bioreactors
Bibliographic record
Abstract
The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic highlighted the importance of vaccine innovation in public health. Hundreds of vaccines built on numerous technology platforms have been rapidly developed against SARS-CoV-2 since 2020. Like all vaccine platforms, an important bottleneck to viral-vectored vaccine development is manufacturing. Here, we describe a scalable manufacturing protocol for replication-competent SARS-CoV-2 Spike-pseudotyped vesicular stomatitis virus (S-VSV)–vectored vaccines using Vero cells grown on microcarriers in a stirred-tank bioreactor. Using Cytodex 1 microcarriers over 6 days of fed-batch culture, Vero cells grew to a density of 3.95 ± 0.42 ×10 6 cells/mL in 1-L stirred-tank bioreactors. Ancestral strain S-VSV reached a peak titer of 2.05 ± 0.58 ×10 8 plaque-forming units (PFUs)/mL at 3 days postinfection. When compared to growth in plate-based cultures, this was a 29-fold increase in virus production, meaning a 1-L bioreactor produces the same amount of virus as 1,284 plates of 15 cm. In addition, the omicron BA.1 S-VSV reached a peak titer of 5.58 ± 0.35 × 10 6 PFU/mL. Quality control testing showed plate- and bioreactor-produced S-VSV had similar particle-to-PFU ratios and elicited comparable levels of neutralizing antibodies in immunized hamsters. This method should enhance preclinical and clinical development of pseudotyped VSV-vectored vaccines in future pandemics.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".