Intranasal delivery of low-dose anti-CD124 antibody enhances treatment of chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
Frequent injections of anti-CD124 monoclonal antibody (αCD124) over long periods of time are used to treat chronic rhinosinusitis with nasal polyps (CRSwNP). Needle-free, intranasal administration (i.n.) of αCD124 is expected to provide advantages of localized delivery, improved efficacy, and enhanced medication adherence. However, delivery barriers such as the mucus and epithelium in the nasal tissue impede penetration of αCD124. Herein, two novel protamine nanoconstructs: allyl glycidyl ether conjugated protamine (Nano-P) and polyamidoamine-linked protamine (Dendri-P) were synthesized and showed enhanced αCD124 penetration through multiple epithelial layers compared to protamine in mice. αCD124 was mixed with Nano-P or Dendri-P and then intranasally delivered for the treatment of severe CRSwNP in mice. Micro-CT and pathological changes in nasal turbinates showed that these two nano-formulations achieved ∼50 % and ∼40 % reductions in nasal polypoid lesions and eosinophil count, respectively. Both nano-formulations provided enhanced efficacy in suppressing nasal and systemic Immunoglobulin E (IgE) and nasal type 2 inflammatory biomarkers, such as interleukin 13 (IL-13) and IL-25. These effects were superior to those in the protamine formulation group and subcutaneous (s.c.) αCD124 given at a 12.5-fold higher dose. Intranasal delivery of protamine, Nano-P, or Dendri-P did not induce any measurable toxicities in mice.
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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 | 0.001 |
| 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".