Evaluation of mucosal adjuvants and immunization routes for the induction of systemic and mucosal humoral immune responses in macaques
Bibliographic record
Abstract
Delivering vaccine antigens to mucosal surfaces is potentially very attractive, especially as protection from mucosal infections may be mediated by local immune responses. However, to date mucosal immunization has had limited successes, with issues of both safety and poor immunogenicity. One approach to improve immunogenicity is to develop adjuvants that are effective and safe at mucosal surfaces. Differences in immune responses between mice and men have overstated the value of some experimental adjuvants which have subsequently performed poorly in the clinic. Due to their closer similarity, non-human primates can provide a more accurate picture of adjuvant performance. In this study we immunised rhesus macaques (Macaca mulatta) using a unique matrix experimental design that maximised the number of adjuvants screened while reducing the animal usage. Macaques were immunised by the intranasal, sublingual and intrarectal routes with the model protein antigens keyhole limpet haemocyanin (KLH), β-galactosidase (β-Gal) and ovalbumin (OVA) in combination with the experimental adjuvants Poly(I:C), Pam3CSK4, chitosan, Thymic Stromal Lymphopoietin (TSLP), MPLA and R848 (Resiquimod). Of the routes used, only intranasal immunization with KLH and R848 induced a detectable antibody response. When compared to intramuscular immunization, intranasal administration gave slightly lower levels of antigen specific antibody in the plasma, but enhanced local responses. Following intranasal delivery of R848, we observed a mildly inflammatory response, but no difference to the control. From this we conclude that R848 is able to boost antibody responses to mucosally delivered antigen, without causing excess local inflammation.
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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 | 0.000 |
| 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 | 0.001 | 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".