Detection Of Anti-Natalizumab Antibodies In Treated Multiple Sclerosis Patients Using a Biacore™ Platform (P4.139)
Bibliographic record
Abstract
OBJECTIVE: To develop a Biacore™-based assay to determine the presence and evolution of anti-natalizumab antibodies in treated multiple sclerosis (MS) patients BACKGROUND: Natalizumab (Tysabri®), a therapeutic humanized monoclonal antibody against α4-integrin, blocks leukocyte migration across the blood-brain barrier into the CNS and suppresses the inflammatory response in relapsing-remitting MS. Persistent anti-natalizumab antibodies are associated with loss of treatment efficacy and increased infusion-related adverse events. Biacore™, a label-free biosensor, detects biomolecular interactions in real time using surface plasmon resonance technology. METHODS: Natalizumab was immobilized on a sensor chip surface and levels of antibodies binding to the surface were determined in diluted samples. We blindly analyzed 10 control samples of varying 12C4 monoclonal antibody concentrations (Biogen Idec), 22 untreated MS patient sera, two positive control sera, and normal healthy individuals (n=20), using mean+3SD of healthy controls as a cutoff for positivity. Additionally, serial samples (n=75) from 7 patients treated with natalizumab for up to 30 months were tested. All samples were also assayed with a modified bridging ELISA originally developed by Biogen Idec. We further confirmed antibody positivity by pre-incubating samples with excess natalizumab, prior to assaying. RESULTS: Eight of 10 Biogen 12C4 control samples (0.65 to 2.50μg/ml) were positive for anti-natalizumab antibodies and two (0μg/ml) were antibody-negative. Biacore™ binding responses and ELISA OD values correlated strongly (R2=0.99903 and R2=0.98037, respectively) with 12C4 concentrations. Furthermore, the untreated MS patient sera were all found to be antibody-negative by both assays. Among the serial samples, one patient was found to have anti-natalizumab antibodies peaking by month six and seroconverting to antibody negative by month 21. In antibody-positive sera, binding levels were decreased or completely abrogated in the presence of excess natalizumab. CONCLUSIONS: A rapid, sensitive, and specific assay has been developed to detect anti-natalizumab antibodies in human serum, and will be further used to investigate the affinity and IgG subclasses of these antibodies. We thank Biogen Idec for providing anti-natalizumab control monoclonal antibody.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".