Membrane Chromatography-Based Downstream Processing for Cell-Culture Produced Influenza Vaccines
Bibliographic record
Abstract
New influenza strains are constantly emerging, causing seasonal epidemics and raising concerns to the risk of a new global pandemic. Since vaccination is an effective method to prevent the spread of the disease and reduce its severity, the development of robust bioprocesses for producing pandemic influenza vaccines is exceptionally important. Herein, a membrane chromatography-based downstream processing platform with a demonstrated industrial application potential was established. Cell culture-derived influenza virus H1N1/A/PR/8/34 was harvested from benchtop bioreactor cultures. For the clarification of the cell culture broth, a depth filtration was selected as an alternative to centrifugation. After inactivation, an anion exchange chromatography membrane was used for viral capture and further processing. Additionally, two pandemic influenza virus strains, the H7N9 subtype of the A/Anhui/1/2013 and H3N2/A/Hong Kong/8/64, were successfully processed through similar downstream process steps establishing optimized process parameters. Overall, 41.3-62.5% viral recovery was achieved, with the removal of 86.3-96.5% host cell DNA and 95.5-99.7% of proteins. The proposed membrane chromatography purification is a scalable and generic method for the processing of different influenza strains and is a promising alternative to the current industrial purification of influenza vaccines based on ultracentrifugation methodologies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".