Assessment of immunogenicity from galcanezumab phase 3 trials in patients with episodic or chronic migraine
Bibliographic record
Abstract
BACKGROUND: This analysis characterizes the immunogenicity profile of galcanezumab, a humanized monoclonal antibody that selectively binds calcitonin gene-related peptide and inhibits its activity, in phase 3 migraine trials. METHODS: Immunogenicity data were analyzed from baseline and double-blind, placebo-controlled phases of the 3-month chronic migraine study REGAIN, the 6-month episodic migraine studies EVOLVE-1 and EVOLVE-2, and from baseline and open-label phases of the 12-month chronic and episodic migraine Study CGAJ. The incidence of baseline antidrug antibodies, treatment-emergent antidrug antibodies, neutralizing antidrug antibodies, and the effect of antidrug antibody titer on pharmacokinetics and pharmacodynamics were assessed. The relationship between antidrug antibody status and efficacy was explored using average change in monthly migraine headache days. Safety analyses assessed the potential relationship between treatment-emergent antidrug antibodies and hypersensitivity events or adverse events related to injection sites. FINDINGS: Across studies, 5.9-11.2% of patients had baseline antidrug antibodies. The incidence of treatment-emergent antidrug antibodies was 2.6-12.4% in the galcanezumab group and 0.5-1.7% in the placebo group. The majority of treatment-emergent antidrug antibodies were detected approximately 3-6 months after first study drug dose. Overall, the observed antidrug antibody titer did not impact galcanezumab concentrations, calcitonin gene-related peptide concentrations, or galcanezumab efficacy. There was no evidence that hypersensitivity events or adverse events related to injection sites were mediated by treatment-emergent antidrug antibodies. INTERPRETATION: These data showed that immunogenicity did not impact galcanezumab concentrations, calcitonin gene-related peptide concentrations, or the efficacy and hypersensitivity profile of galcanezumab in patients with migraine.
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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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".