Establishing a Robust Manufacturing Platform for Recombinant Veterinary Vaccines: An Adenovirus-Vector Vaccine to Control Newcastle Disease Virus Infections of Poultry in Sub-Saharan Africa
Bibliographic record
Abstract
Developing vaccine technology platforms to respond to pandemic threats or zoonotic diseases is a worldwide high priority. The risk of infectious diseases transmitted from wildlife and domestic animals to humans makes veterinary vaccination and animal health monitoring highly relevant for the deployment of public health global policies in the context of “one world, one health” principles. Sub-Saharan Africa is frequently impacted by outbreaks of poultry diseases such as avian influenza and Newcastle Disease (ND). Here, an adenovirus-vectored vaccine technology platform is proposed for rapid adaptation to ND or other avian viral threats in the region. Ethiopian isolates of the Newcastle Disease virus (NDV) were subjected to sequence and phylogenetic analyses, enabling the construction of antigenically matched vaccine candidates expressing the fusion (F) and hemagglutinin-neuraminidase (HN) proteins. A cost-effective vaccine production process was developed using HEK293 cells in suspension and serum-free medium. Productive infection in bioreactors (1–3 L) at 2 × 106 cells/mL resulted in consistent infectious adenoviral vector titers of approximately 5–6 × 108 TCID50/mL (approximately 1011VP/mL) in the harvest lysates. Groups of chickens were twice immunized with 1 × 1010 TCID50 of the vectors, and full protection against a lethal NDV challenge was provided by the vector expressing the F antigen. These results consolidate the basis for a streamlined and scalable-vectored vaccine manufacturing process for deployment in low- and medium-income countries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".