Spray Drying and Particle Engineering in Dosage Form Design for Global Vaccines
Bibliographic record
Abstract
Vaccines are a very important tool in the effort to reduce the global burden of infectious diseases. Modern vaccines can be formulated in several ways to induce specific immunity, including through the use of live bacteria, subunit antigens, and even genetic material. However, vaccines typically need to be transported and stored under controlled refrigerated or frozen conditions to maintain potency. This strict temperature control is incompatible with the available infrastructure in many developing countries. One method of improving the thermostability of a vaccine is through drying of a liquid presentation into a dry dosage form. In addition to enhancing the capability for distribution in resource-poor settings, these dry vaccine forms are more suitable for long-term stockpiling. Spray drying is a drying method that has been successfully used to stabilize many experimental vaccines into a dry form for storage above refrigerated temperatures. Additionally, the use of spray drying allows for the production of engineered particles suitable for respiratory administration. These particles can be further designed for increased out-of-package robustness against high humidity. Furthermore, there are already commercial dry powder delivery devices available that can be used to safely deliver vaccines to the respiratory system. The research in this field demonstrates that the resources to develop highly stable vaccines in flexible dosage forms are available and that these presentations offer many advantages for global vaccination campaigns.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".