Recent advances and current challenges in process intensification of cell culture‐based influenza virus vaccine manufacturing
Bibliographic record
Abstract
Abstract Every year, millions of people are infected by the influenza virus around the world, which results in more than half a million deaths, particularly among the more vulnerable population. Since vaccination is the most efficient method of protection, millions of doses must be produced in a short period of time to supply seasonal vaccination campaigns around the globe, and billions of doses would be required to respond to a potential global influenza pandemic. The lack of flexibility of the current egg‐based production system and its long production cycles have pushed biomanufacturers to invest in more flexible alternatives for vaccine production, particularly cell culture‐based processes. While a valuable alternative, virus yields are still low, requiring extensive efforts to increase process productivity. Major efforts in the intensification of cell culture‐based viral vaccine manufacturing focus on the development of high cell density processes, which mostly involve the employment of perfusion‐based strategies. In this review, some of the advantages of cell culture‐based production of influenza vaccines will be discussed, and some of the current challenges and opportunities for the intensification of these processes will be presented. Finally, the recent advances in high cell density processes for influenza vaccine manufacturing will be reviewed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".