Dupilumab as an Adjunct to Oral Immunotherapy in Pediatric Patients With Peanut Allergy
Bibliographic record
Abstract
BACKGROUND: Peanut allergy is a common, life-threatening food allergy in children. We evaluated whether dupilumab, which blocks the activity of interleukin (IL)-4/IL-13, enhances the efficacy of oral immunotherapy (OIT) AR101 in pediatric patients with peanut allergy. METHODS: A Phase II, multicenter, randomized, double-blind study was conducted in the USA (NCT03682770) in pediatric patients (6-≤ 17 years old) with confirmed peanut allergy. Patients were randomized 2:1 to receive dupilumab + OIT or placebo + OIT during a 28-40-week up-dosing period. Patients in the dupilumab + OIT group were re-randomized 1:1 and received dupilumab + OIT or placebo + OIT during 24-week OIT maintenance, undergoing a 2044 mg (cumulative) of peanut protein double-blind, placebo-controlled food challenge (DBPCFC) following up-dosing, maintenance, and at 12-week post-treatment follow-up. RESULTS: The study enrolled 148 patients, 123 of whom were included in the modified full analysis set, with a mean age of 11.1 years. Dupilumab + OIT treatment (n = 84) led to a 20.2% increase (p < 0.05) in the number of patients who passed a DBPCFC to 2044 mg (cumulative) of peanut protein following the up-dosing period versus placebo (OIT alone, n = 39). Following the OIT maintenance period, continuous dupilumab treatment improved the number of patients who passed a DBPCFC to 2044 mg (cumulative) of peanut protein versus patients continuously on OIT alone (16.6% difference [95% CI -9.7, 42.8], p = 0.2123). Safety was consistent with known dupilumab safety profile. CONCLUSIONS: Dupilumab provided a modest increase efficacy of OIT in children and adolescents with peanut allergy, though it did not provide protection against OIT-related anaphylaxis. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03793608.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".