A Virus-Like Particle-Based Anti-Nerve Growth Factor Vaccine Reduces Inflammatory Hyperalgesia: Potential Long-Term Therapy for Chronic Pain
Bibliographic record
Abstract
Chronic pain resulting from inflammatory and neuropathic disorders causes considerable economic and social burden. For a substantial proportion of patients, conventional drug treatments do not provide adequate pain relief. Consequently, novel approaches to pain management, involving alternative targets and new therapeutic modalities compatible with chronic use, are being sought. Nerve growth factor (NGF) is a major mediator of chronic pain. Clinical testing of NGF antagonists is ongoing, and clinical proof of concept has been established with a neutralizing mAb. Active immunization, with the goal of inducing therapeutically effective neutralizing autoreactive Abs, is recognized as a potential treatment option for chronic diseases. We have sought to determine if such a strategy could be applied to chronic pain by targeting NGF with a virus-like particle (VLP)-based vaccine. A vaccine comprising recombinant murine NGF conjugated to VLPs from the bacteriophage Qβ (NGFQβ) was produced. Immunization of mice with NGFQβ induced anti-NGF-specific IgG Abs capable of neutralizing NGF. Titers could be sustained over 1 y by periodic immunization but declined in the absence of boosting. Vaccination with NGFQβ substantially reduced hyperalgesia in collagen-induced arthritis or postinjection of zymosan A, two models of inflammatory pain. Long-term NGFQβ immunization did not change sensory or sympathetic innervation patterns or induce cholinergic deficits in the forebrain, nor did it interfere with blood-brain barrier integrity. Thus, autovaccination targeting NGF using a VLP-based approach may represent a novel modality for the treatment of chronic pain.
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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 | 0.001 | 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".