Overview of recent advances in Vero cells genomic characterization and engineering for high‐throughput vaccine manufacturing
Bibliographic record
Abstract
Abstract Background The Vero cell line is the most used continuous cell line for viral vaccine manufacturing with more than 30 years of accumulated experience in the vaccine industry. Nonetheless, virus production yield with Vero cells remains limited. Therefore, given Vero cell line infection susceptibility to a wide range of viruses, alleviating limitations of viral replication and increasing production kinetics in Vero cells could significantly reduce vaccine production time and cost. Here we review the literature on the development of Vero cells as the most effective manufacturing platform for viral vaccines. Whereas various bioprocess development strategies were proposed to improve the production, this review focuses on the genomic characterization and genetic engineering aspects of Vero cells. Rational design of production cell line emerged as a state‐of‐the‐art approach to significantly enhance Vero cell viral production yield and adaptation to suspension culture which aligns with the global preparedness efforts to accelerate and intensify vaccine production capacity to better respond to pandemic situations and epidemic outbreaks. Conclusion Until recently, the lack of a reference genome for the Vero cell line has limited the understanding of Vero cells behavior in defined culture conditions as well as host‐virus interactions underlying the affinity of the Vero cell line with emerging and re‐emerging pathogens. Importantly this limited our ability to re‐design high‐yield vaccine production processes using Vero genome editing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".