Selection of a Novel Anti-Nicotine Vaccine: Influence of Antigen Design on Antibody Function in Mice
Bibliographic record
Abstract
Anti-nicotine vaccines may aid smoking cessation via the induction of anti-nicotine antibodies (Ab) which reduce nicotine entering the brain, and hence the associated reward. Ab function depends on both the quantity (titer) and the quality (affinity) of the Ab. Anti-nicotine vaccines tested previously in clinical studies had poor efficacy despite high Ab titer, and this may be due to inadequate function if Ab of low affinity were induced. In this study, we designed and synthesized a series of novel nicotine-like haptens which were all linked to diphtheria toxoid (DT) as carrier, but which differed in the site of attachment of linker to nicotine, the nature of linker used, and the handle used to attach the hapten to DT. The resulting hapten conjugates were evaluated in a mouse model, using CpG (a TLR9 agonist) and aluminum hydroxide (Al(OH)3) as adjuvants, whereby Ab titers, affinity and function were evaluated using a radiolabeled nicotine challenge model. A series of additional linkers varying in length, rigidity and polarity were used with a single hapten to generate additional DT-conjugates, which were also tested in mice. Conjugates made with different haptens resulted in various titers of anti-nicotine Ab. Several haptens gave similarly high Ab titers, but among these, Ab affinity and hence function varied considerably. Linker also influenced Ab titer, affinity and function. These results demonstrate that immune responses induced in mice by nicotine-conjugate antigens are greatly influenced by hapten design including site of attachment of linker to nicotine, the nature of linker used, and the handle used to attach the hapten to DT. While both Ab titer and affinity contributed to function, affinity was more sensitive to antigen differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".