Effect of <i>HLA-DRB1</i> alleles and genetic variants on the development of neutralizing antibodies to interferon beta in the BEYOND and BENEFIT trials
Bibliographic record
Abstract
Background: Treatment of multiple sclerosis (MS) with interferon β can lead to the development of antibodies directed against interferon β that interfere with treatment efficacy. Several observational studies have proposed different HLA alleles and genetic variants associated with the development of antibodies against interferon β. Objective: To validate the proposed genetic markers and to identify new markers. Methods: Associations of genetic candidate markers with antibody presence and development were examined in a post hoc analysis in 941 patients treated with interferon β-1b in the Betaferon ® Efficacy Yielding Outcomes of a New Dose (BEYOND) and BEtaseron ® /BEtaferon ® in Newly Emerging multiple sclerosis For Initial Treatment (BENEFIT) prospective phase III trials. All patients were treated with interferon β-1b for at least 6 months. In addition, a genome-wide association study was conducted to identify new genetic variants. Results: We confirmed an increased risk for carriers of HLA-DRB1*04:01 (odds ratio (OR) = 3.3, p = 6.9 × 10 −4 ) and HLA-DRB1*07:01 (OR = 1.8, p = 3.5 × 10 −3 ) for developing neutralizing antibodies (NAbs). Several additional, previously proposed HLA alleles and genetic variants showed nominally significant associations. In the exploratory analysis, variants in the HLA region were associated with NAb development at genome-wide significance (OR = 2.6, p = 2.30 × 10 −15 ). Conclusion: The contribution of HLA alleles and HLA-associated single-nucleotide polymorphisms (SNPs) to the development and titer of antibodies against interferon β was confirmed in the combined analysis of two multi-national, multi-center studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".