Thermostabilization of a model viral-vectored oral thin film vaccine
Bibliographic record
Abstract
Vaccines rely on a global cold chain to maintain vaccine potency throughout the product life cycle. Existing vaccine thermostabilization methods like lyophilization and spray-drying impart significant stress on the vaccine, reducing its potency. Therefore, dissolvable oral thin films (OTFs) have emerged as an alternative thermostabilizing vaccine delivery platform wherein the vaccine is immobilized in a polymer-sugar matrix and administered to the oral mucosa. Herein, we demonstrate the feasibility of incorporating a model adenovirus vector into an OTF (Ad5 OTF) using a simple one-hour solvent casting process, and we demonstrate retention of the adenovirus infectious titer during storage, as assessed by flow cytometric titering. Increasing Tris buffer concentration and changing the surfactant from a nonionic Tween 80 to a zwitterionic poly(maleic anhydride- alt -1-octadecene) substituted with 3-(dimethylamino)propylamine (PMAL) and increasing its concentration improved the six-day ambient temperature stability by nearly 4-fold. A 2 3 full factorial design of experiment investigating the influence of trehalose, PEG, and PMAL concentration demonstrated that PMAL and trehalose concentration have the greatest impact on stability of Ad5 OTFs, improving the six-day ambient temperature stability by 250-fold, compared to the first formulation evaluated herein. The thermal stabilization of Ad5 OTFs prepared with a simple one-hour casting process demonstrates the scale-up and scale-out potential for this OTF formulation as a vaccine delivery platform, improving the accessibility of vaccines.
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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 | 0.000 |
| 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".