Frequency and Magnitude of Interferon β Neutralizing Antibodies in the Evaluation of Interferon β Immunogenicity in Patients with Multiple Sclerosis
Bibliographic record
Abstract
Patients with multiple sclerosis (MS) treated with interferon β (IFNβ) preparations develop varying levels of antibodies that neutralize the biological effects of IFNβ, reduce its in vivo bioavailability, and diminish its therapeutic efficacy. The aim was to determine as distinct measures of immunogenicity the occurrence (frequency) and the magnitude (level) of IFNβ neutralizing antibody (NAb) formation in a large Canadian population as a cross-sectional study of patients with MS treated in a clinical practice setting with different, equally available IFNβ products: Avonex(®) (intramuscular IFNβ-1a), Rebif(®) (subcutaneous (SC) IFNβ-1a) at 22 and 44 μg, and Betaseron(®) (SC IFNβ-1b). Over a 3-year period 3,124 serum samples from 2,711 patients with MS were submitted by neurologists in MS clinics distributed across Canada and tested for NAbs in a single independent laboratory, utilizing a quantitative, standardized NAb bioassay. NAb frequency was greatest (35%) with Rebif (SC IFNβ-1a) 44 μg and least (7.5%) with Avonex (intramuscular IFNβ-1a), whereas Betaseron (IFNβ-1b) and Rebif 22 μg were in between (22%). NAb serum levels at magnitudes considered high, ≥100 tenfold reduction units (TRU)/mL, were found in 65%-83% of patients with detectable NAbs. Nearly half (42%-47%) of NAb-positive patients given IFNβ-1a preparations had very high titers (≥ 1,000 TRU/mL), whereas only 22% of NAb-positive patients on Betaseron had titers >1,000 TRU/mL. Differences in patterns of NAb formation among the four IFNβ product-dose combinations became more evident in patients with MS when both NAb frequency and the full range of NAb titer magnitude were measured.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".