Formulation development of a stable influenza recombinant neuraminidase vaccine candidate
Bibliographic record
Abstract
Current influenza vaccines could be augmented by including recombinant neuraminidase (rNA) protein antigen to broaden protective immunity and improve efficacy. Toward this goal, we investigated formulation conditions to optimize rNA physicochemical stability. When rNA in sodium phosphate saline buffer (NaPBS) was frozen and thawed (F/T), the tetrameric structure transitioned from a "closed" to an "open" conformation, negatively impacting functional activity. Hydrogen deuterium exchange experiments identified differences in anchorage binding sites at the base of the open tetramer, offering a structural mechanistic explanation for the change in conformation and decreased functional activity. Change to the open configuration was triggered by the combined stresses of acidic pH and F/T. The desired closed conformation was preserved in a potassium phosphate buffer (KP), minimizing pH drop upon freezing and including 10% sucrose to control F/T stress. Stability was further evaluated in thermal stress studies where changes in conformation were readily detected by ELISA and size exclusion chromatography (SEC). Both tests were suitable indicators of stability and antigenicity and considered potential critical quality attributes (pCQAs). To understand longer-term stability, the pCQA profiles from thermally stressed rNA at 6 months were modeled to predict stability of at least 24-months at 5°C storage. In summary, a desired rNA closed tetramer was maintained by formulation selection and monitoring of pCQAs to produce a stable rNA vaccine candidate. The study highlights the importance of understanding and controlling vaccine protein structural and functional integrity.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".