Determination of Deamidation in Adjuvanted Vaccine Antigens through Isoaspartic Acid Quantification
Bibliographic record
Abstract
Deamidation is a post-translational chemical modification that occurs within proteins and can be influenced by many factors, including temperature and pH. In vaccines, deamidation is considered undesirable as it may lead to changes in structure, function, stability, and immunogenicity. Detecting deamidation in vaccines, especially adjuvanted vaccines, can be challenging due to the lack of simple quantitative techniques. In this study, the quantification of isoaspartic acid (isoAsp) was used to assess deamidation in model antigens in the presence and absence of common vaccine adjuvants. This study shows that the detection of isoAsp was possible in the presence of various types of adjuvants with little to no interference. High levels of isoAsp were detected in thermally and pH-stressed adjuvanted vaccines, suggesting significant deamidation and highlighting the stability-indicating capabilities of the assay. The quantification of isoAsp in stability programs of a vaccine drug product could possibly find applications in product shelf-life determination, using thermal kinetic modeling to predict deamidation over time. The ability to detect deamidation early in vaccine development enhances process improvements and ultimately improves the vaccine's stability. To summarize, this paper describes a rapid and simple method to determine deamidation in adjuvanted vaccines. This method could be applicable to formulation development, stability assessment, or shelf-life determination.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".