The effect of preexisting anti-carrier immunity on subsequent responses to CRM<sub>197</sub>or Qb-VLP conjugate vaccines
Bibliographic record
Abstract
CONTEXT: Certain antigens, such as haptens (small molecules), short peptides, and carbohydrates (e.g. bacterial polysaccharides) are non- or poorly immunogenic unless conjugated to a carrier molecule that provides a structural scaffold for antigen presentation as well as T cell help required for B-cell activation and maturation. However, the carriers themselves are immunogenic and resulting carrier-specific immune responses may impact the immunogenicity of other conjugate vaccines using the same carrier that are administered subsequently. OBJECTIVE: Herein, using two different carriers (cross-reactive material 197, CRM and Qb-VLP), we examined in mice the impact that preexisting anti-carrier antibodies (Ab) had on subsequent immune responses to conjugates with either the same or a different carrier. METHOD: For this purpose, we used two nicotine hapten conjugates (NIC7-CRM or NIC-Qb), two IgE peptide conjugates (Y-CRM or Y-Qb), and a pneumococcal polysaccharide conjugate (Prevnar 13(®)). RESULTS: Prior exposure to CRM or Qb-VLP significantly reduced subsequent responses to the conjugated antigen having the homologous carrier, with the exception of Prevnar 13® where anti-polysaccharide responses were similar to those in animals without preexisting anti-carrier Ab. CONCLUSION: Collectively, the data suggest that the relative sizes of the antigen and carrier, as well as the conjugation density for a given conjugate impact the extent of anti-carrier suppression. All animals developed anti-carrier responses with repeat vaccination and the differences in Ab titer between groups with and without preexisting anti-carrier responses became less apparent; however, anti-carrier effects were more durable for Ab function.
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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) | 0.002 | 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".