Efficacy and Safety of Dupilumab in Children With Peanut Allergy: A Multicenter, Open‐Label, Phase <scp>II</scp> Study
Bibliographic record
Abstract
BACKGROUND: Peanut allergy is a potentially life-threatening food allergy in children. This study explored whether dupilumab, a human monoclonal immunoglobulin (Ig)G4 antibody that blocks the activity of interleukin (IL)-4/IL-13, improved safety and desensitization to peanut exposure in children with peanut allergy. METHODS: A Phase II, 24-week, multicenter, single-arm, open-label, proof-of-concept study was conducted in the USA and Canada (NCT03793608). Children/adolescents with peanut allergy received subcutaneous dupilumab 300 mg (≥ 60 kg) or 200 mg (≥ 20 to < 60 kg) every 2 weeks. The primary endpoint was the proportion of participants who passed a double-blind placebo-controlled food challenge (DBPCFC) with ≥ 444 mg (cumulative) of peanut protein at week 24. Secondary endpoints included safety measures (Consortium of Food Allergy Research grading system) and change from baseline in peanut-specific (ps)-IgG4, total IgE, and ps-IgE. RESULTS: Twenty-four participants enrolled and received dupilumab: 75.0% were male, 79.2% were white, mean (standard deviation) age was 11.7 (3.3) years. Most (95.8%) participants had not received allergen immunotherapy. Two participants (8.3%) achieved the primary endpoint and passed the DBPCFC at week 24. Fifteen participants (62.5%) reported 66 treatment-emergent adverse events, all being mild or in moderate intensity. At the week 24 DBPCFC, 8 participants (33.3%) had a grade 2 allergic reaction (no grade 3 or above); 10 (41.7%) used adrenaline as a rescue medication. Dupilumab treatment resulted in a median reduction of total and ps-IgE of -54% and -49%, respectively, and a 0% change in ps-IgG4. CONCLUSIONS: Dupilumab monotherapy treatment for 24 weeks did not improve desensitization to peanut exposure after food challenge.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".