The incidence and significance of anti-natalizumab antibodies
Bibliographic record
Abstract
Objective: To determine the incidence and clinical effects of antibodies that develop during treatment with natalizumab. Methods: In two randomized, double-blind, placebo-controlled studies (natalizumab safety and efficacy in relapsing remitting multiple sclerosis [MS, AFFIRM] and safety and efficacy of natalizumab in combination with interferon β-1a [INFβ1a] in patients with relapsing remitting MS [SENTINEL]) of patients with relapsing multiple sclerosis, blood samples were obtained at baseline and every 12 weeks to determine the presence of antibodies against natalizumab. Antibodies to natalizumab were measured using an ELISA. Patients were categorized as “transiently positive” if they had detectable antibodies (≥0.5 μg/mL) at a single time point or “persistently positive” if they had antibodies at two or more time points ≥6 weeks apart. Results: In the AFFIRM study, antibodies were detected in 57 of 625 (9%) of natalizumab-treated patients: Twenty (3%) were transiently positive and 37 (6%) were persistently positive. Persistently positive patients showed a loss of clinical efficacy as measured by disability progression (p ≤ 0.05), relapse rate (p = 0.009), and MRI (p ≤ 0.05) compared with antibody-negative patients. In transiently positive patients, full efficacy was achieved after approximately 6 months of treatment, the time when patients were becoming antibody negative. The incidence of infusion-related adverse events was significantly higher in persistently positive patients. Results of SENTINEL were similar to AFFIRM, except with regard to sustained disability progression; differences between persistently positive and antibody-negative patients were not statistically significant. Conclusions: The incidence of persistent antibody positivity associated with natalizumab is 6%. Reduced clinical efficacy is apparent in persistently positive patients. Patients with a suboptimal clinical response or persistent infusion-related adverse events should be considered for antibody testing. GLOSSARY: BLQ = below the limit of quantification; EDSS = Expanded Disability Status Scale; Gd+ = gadolinium enhancing; IFNβ1a = interferon β-1a; MS = multiple sclerosis; MSFC = multiple sclerosis functional composite; OD = optical density.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".