Predicting Probability of Tolerating Discrete Amounts of Peanut Protein in Allergic Children Using Epitope-Specific IgE Antibody Profiling
Bibliographic record
Abstract
This study evaluated if sequential (linear) epitope-specific IgE (ses-IgE) profiling can determine the probability of tolerating discrete amounts of peanut protein in allergic individuals undergoing double-blind, placebo-controlled food challenges (DBPCFC) to peanut by PRACTALL dosing.The study includes 406 peanut-allergic participants ages 4 to 25 years, 75 in the discovery cohort and 331 in the validation cohort, who underwent DBPCFC as part of 5 independent cohorts in multiple countries: BOPI (n = 68), OPIA (n = 56), CAFETERIA (n = 104), CoFAR6 (n = 84), and PEPITES (n = 94).A bead-based assay was used to evaluate epitope sequence and quantify 64 ses-IgE antibodies in blood samples. Regression models determined pairs of ses-IgEs that predicted Cumulative Tolerated Dose (CTD) in the discovery cohort. This epitope predictor was then applied to the validation cohort participants to improve the model generalizability. Individuals were grouped based on their predicted tolerated doses and probabilities of reactions at each CTD threshold were calculated.An algorithm using 2 ses-IgE antibodies (Ara h 2_008 and Ara h 3_100 epitopes) was correlated with CTDs (P = .61; P < .05) in the discovery cohort; the correlation was 0.51 (P < .05) in the validation cohort. Individuals assigned to a “high” dose reactivity group using the ses-IgE algorithm were about 4 times more likely to tolerate a given amount of peanut, compared with those assigned to the “low” dose reactivity group, eg, predicted probabilities of tolerating 4, 14, 44, 144, and 444 mg were 92%, 77%, 53%, 29%, and 10% in the “low” dose reactivity group, compared with 98%, 95%, 94%, 88%, and 73% in the “high” dose reactivity group.An epitope-based predictor accurately identified CTDs and may be a useful surrogate for peanut challenges, despite limitations including small numbers of individuals tolerating each threshold dose and variations in study challenge protocols.Higher IgE diversity has been associated with developing allergic symptoms after consuming smaller amounts of peanut protein. This study used peanut-specific epitopes to predict the probabilities of allergic reactions to different amounts of peanut in children with peanut allergy, generating a validated algorithm for detailed determination of threshold tolerance. This algorithm will inform the utility, safety, and dosing of oral peanut challenges for children with peanut allergy, and in some instances, may be a surrogate for DBPCFC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".