Development of vaccines for treatment of opioid abuse using carrier proteins suitable for pharmaceutical manufacturing
Bibliographic record
Abstract
Abstract In 2016, over 50,000 people died in the US from heroin and prescription opioid related overdoses. Current standard of care is not sufficient. Therapeutic vaccines against opioids are safe, long-lasting, and cost-effective alternatives to opioid-based pharmacotherapy. Opioid vaccines elicit opioid-specific antibodies that prevent opioid distribution to the brain and opioid-induced behavior and toxicity. To ensure translation, we have developed candidate vaccines that include components suitable for good manufacturing practices and have the ability to be characterized according to FDA guidelines. In this study, we synthesized three oxycodone-based (OXY) haptens to assess linker and bioconjugation chemistry in vaccine efficacy against oxycodone in mice. Then, we tested efficacy of lead OXY haptens conjugated to novel carrier proteins consisting of E. coli-expressed diphtheria cross-reactive material (EcoCRM™) and nontoxic tetanus toxin fragment (rTTHc). Haptens using tetraglycine linkers coupled with carbodiimide chemistry elicited the greatest oxycodone specific serum IgG antibody titers and significantly reduced oxycodone distribution to the brain. Oxycodone vaccines containing EcoCRM™ and rTTHc, adsorbed on alum adjuvant, were effective in inducing expansion of oxycodone-specific plasma and germinal center B cells, blocking oxycodone distribution to the brain, and were easily characterized by mass spectrometry. The OXY-EcoCRM™ and OXY-rTTHc were equivalent, or superior, to vaccines containing keyhole limpet hemocyanin protein, tetanus toxoid and CRM197, making them candidate immunogens for further development.
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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".