Low incidence of anti‐drug antibodies in patients with type 2 diabetes treated with once‐weekly glucagon‐like peptide‐1 receptor agonist dulaglutide
Bibliographic record
Abstract
Therapeutic administration of peptides may result in anti-drug antibody (ADA) formation, hypersensitivity adverse events (AEs) and reduced efficacy. As a large peptide, the immunogenicity of once-weekly glucagon-like peptide-1 (GLP-1) receptor agonist dulaglutide is of considerable interest. The present study assessed the incidence of treatment-emergent dulaglutide ADAs, hypersensitivity AEs, injection site reactions (ISRs), and glycaemic control in ADA-positive patients in nine phase II and phase III trials (dulaglutide, N = 4006; exenatide, N = 276; non-GLP-1 comparators, N = 1141). Treatment-emergent dulaglutide ADAs were detected using a solid-phase extraction acid dissociation binding assay. Neutralizing ADAs were detected using a cell-based assay derived from human endothelial kidney cells (HEK293). A total of 64 dulaglutide-treated patients (1.6% of the population) tested ADA-positive versus eight (0.7%) from the non-GLP-1 comparator group. Of these 64 patients, 34 (0.9%) had dulaglutide-neutralizing ADAs, 36 (0.9%) had native-sequence GLP-1 (nsGLP-1) cross-reactive ADAs and four (0.1%) had nsGLP-1 neutralization ADAs. The incidence of hypersensitivity AEs and ISRs was similar in the dulaglutide versus placebo groups. No dulaglutide ADA-positive patient reported hypersensitivity AEs. Because of the low incidence of ADAs, it was not possible to establish their effect on glycaemic control.
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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 | 0.000 | 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) | 0.001 | 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".