Process Development for Newcastle Disease Virus-Vectored Vaccines in Serum-Free Vero Cell Suspension Cultures
Bibliographic record
Abstract
The ongoing COVID-19 pandemic drew global attention to infectious diseases, attracting numerous resources for development of pandemic preparedness plans and vaccine platforms—technologies with robust manufacturing processes that can quickly be pivoted to target emerging diseases. Newcastle Disease Virus (NDV) has been studied as a viral vector for human and veterinary vaccines, but its production relies heavily on embryonated chicken eggs, with very few studies producing NDV in cell culture. Here, NDV is produced in suspension Vero cells, and analytical assays (TCID50 and ddPCR) are developed to quantify infectious and total viral titer. NDV-GFP and NDV-FLS (SARS-CoV-2 full-length spike protein) constructs were adapted to replicate in Vero and HEK293 suspension cultures using serum-free media, while fine-tuning parameters such as MOI, temperature, and trypsin concentration. Shake flask productions with Vero cells resulted in infectious titers of 1.07 × 108 TCID50/mL for NDV-GFP and 1.33 × 108 TCID50/mL for NDV-FLS. Production in 1 L batch bioreactors also resulted in high titers in culture supernatants, reaching 2.37 × 108 TCID50/mL for NDV-GFP and 3.16 × 107 TCID50/mL for NDV-FLS. This shows effective NDV production in cell culture, building the basis for a scalable vectored-vaccine manufacturing process that can be applied to different targets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".