Abatacept to induce remission of peanut allergy during oral immunotherapy (ATARI): protocol for a phase 2a randomized controlled trial
Bibliographic record
Abstract
Context: While oral immunotherapy (OIT) has been shown to promote the remission of mild peanut allergy in young children, there is still an unmet need for a disease-modifying intervention for older patients and those with severe diseases. In mice models, abatacept, a cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) immunoglobulin fusion protein, has been shown to promote immune tolerance to food when used as an adjuvant to allergen immunotherapy. The goal of this study is to explore the potential efficacy of abatacept in promoting immune tolerance to food allergens during OIT in humans. Methods: In this phase 2a proof-of-concept study (NCT04872218), 14 peanut-allergic participants aged from 14 to 55 years will be randomized at a 1:1 ratio to abatacept vs. placebo for the first 24 weeks of a peanut OIT treatment (target maintenance dose of 300 mg peanut protein). The primary outcome will be the suppression of the OIT-induced surge in peanut-specific IgE/total IgE at 24 weeks, relative to the baseline. Sustained unresponsiveness will be assessed as a secondary outcome starting at 36 weeks by observing incremental periods of peanut avoidance followed by oral food challenges. Discussion: This is the first study assessing the use of abatacept as an adjuvant to allergen immunotherapy in humans. As observed in preclinical studies, the ability of abatacept to modulate the peanut-specific immune response during OIT will serve as a proxy outcome for the development of clinical tolerance, given the small sample size. The study will also test a new patient-oriented approach to sustained tolerance testing in randomized controlled trials.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".