Enhancing the Immune Response of a Nicotine Vaccine with Synthetic Small “Non-Natural” Peptides
Bibliographic record
Abstract
The addictive nature of nicotine is likely the most significant reason for the continued prevalence of tobacco smoking despite the widespread reports of its negative health effects. Nicotine vaccines are an alternative to the currently available smoking cessation treatments, which have limited efficacy. However, the nicotine hapten is non-immunogenic, and successful vaccine formulations to treat nicotine addiction require both effective adjuvants and delivery systems. The immunomodulatory properties of short, non-natural peptide sequences not found in human systems and their ability to improve vaccine efficacy continue to be reported. The aim of this study was to determine if small “non-natural peptides,” as part of a conjugate nicotine vaccine, could improve immune responses. Four peptides were synthesized via solid phase methodology, purified, and characterized. Ex vivo plasma stability studies using RP-HPLC confirmed that the peptides were not subject to proteolytic degradation. The peptides were formulated into conjugate nicotine vaccine candidates along with a bacterial derived adjuvant vaccine delivery system and chitosan as a stabilizing compound. Formulations were tested in vitro in a dendritic cell line to determine the combination that would elicit the greatest 1L-1β response using ELISAs. Three of the peptides were able to enhance the cytokine response above that induced by the adjuvant delivery system alone. In vivo vaccination studies in BALB/c mice demonstrated that the best immune response, as measured by nicotine-specific antibody levels, was elicited from the conjugate vaccine structure, which included the peptide, as well as the other components. Isotype analyses highlighted that the peptide was able to shift immune response toward being more humorally dominant. Overall, the results have implications for the use of non-natural peptides as adjuvants not only for the development of a nicotine vaccine but also for use with other addictive substances and conventional vaccination targets as well.
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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) | 0.001 | 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".