The combination of very-small size proteoliposomes and alum is a safe adjuvant alternative for inducing anti-EGF antibodies: a preclinical study
Bibliographic record
Abstract
Immunization with human recombinant EGF chemically bound to the P64k protein of Neisseria meningitides (hrEGF-P64k) and adjuvanted in Montanide ISA 51 VG (Montanide) is an efficient strategy to induce polyclonal antibodies (PAbs) response targeting this self -antigen in cancer patients, which is the basis of the CIMAvax-EGF vaccine. The neutralizing potential of EGF-specific induced PAbs supports promising clinical data obtained to date with this vaccine. Herein, we evaluated a combination of very small-size proteoliposomes (VSSP) and aluminum hydroxide (Alum) as a novel adjuvant to induce specific PAbs with neutralizing and anti-proliferative properties on tumor cells, considering EGF as a model antigen. Toxicity at the injection site was not detected for the vaccine formulation containing VSSP/Alum, and it was immunogenic in BALB/c mice, as evidenced by the induction of high titers of EGF-specific polyclonal antibodies (PAbs). While schedule optimization increased the magnitude of the PAbs response induced by VSSP/Alum, induced PAbs’s avidity and intrinsic neutralizing potential were comparable to the humoral response induced by Montanide. Also, VSSP addition switched IgG subclasses distribution into a Th1-like pattern, as obtained with Montanide and desirable for a cancer vaccine. Finally, equivalent PAbs titers were induced by the vaccine formulations adjuvanted in VSSP/Alum or Montanide in tumor-bearing-mice, and immunosuppressed mice, suggesting the feasibility of the VSSP/Alum combined adjuvant for inducing anti-EGF antibodies in cancer patients at advanced stages of the disease.
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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) | 0.000 | 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".