Vero cell upstream bioprocess development for the production of viral vectors and vaccines
Bibliographic record
Abstract
The Vero cell line is considered the most used continuous cell line for the production of viral vectors and vaccines. Historically, it is the first cell line that was approved by the WHO for the production of human vaccines. Comprehensive experimental data on the production of many viruses using the Vero cell line can be found in the literature. However, the vast majority of these processes is relying on the microcarrier technology. While this system is established for the large-scale manufacturing of viral vaccine, it is still quite complex and labor intensive. Moreover, scale-up remains difficult and is limited by the surface area given by the carriers. To overcome these and other drawbacks and to establish more efficient manufacturing processes, it is a priority to further develop the Vero cell platform by applying novel bioprocess technologies. Especially in times like the current COVID-19 pandemic, advanced and scalable platform technologies could provide more efficient and cost-effective solutions to meet the global vaccine demand. Herein, we review the prevailing literature on Vero cell bioprocess development for the production of viral vectors and vaccines with the aim to assess the recent advances in bioprocess development. We critically underline the need for further research activities and describe bottlenecks to improve the Vero cell platform by taking advantage of recent developments in the cell culture engineering field.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".