Effect of Dupilumab on Blood Eosinophil Counts in Patients With Asthma, Chronic Rhinosinusitis With Nasal Polyps, Atopic Dermatitis, or Eosinophilic Esophagitis
Bibliographic record
Abstract
BACKGROUND: Transient increases in blood eosinophil counts have been observed in dupilumab clinical trials. OBJECTIVE: To assess eosinophil counts and eosinophilia-related treatment-emergent adverse events (TEAEs) across 11 dupilumab clinical trials, comparing adult and adolescent patients with asthma and adult patients with chronic rhinosinusitis with nasal polyps (CRSwNP), atopic dermatitis, and eosinophilic esophagitis. METHODS: Eosinophil counts, rates of eosinophilia-related TEAEs or treatment-emergent eosinophilia (>1,500 cells/μL), discontinuations, clinical symptoms, and efficacy in patients with asthma or CRSwNP with treatment-emergent eosinophilia are presented. RESULTS: Transient increases in mean eosinophil counts were observed in dupilumab-treated patients with asthma (mean range across studies at baseline: 349-370 cells/μL; week 4: 515-578 cells/μL), CRSwNP (baseline: 440-448 cells/μL; week 16: 595 cells/μL), and atopic dermatitis (baseline: 434-600 cells/μL; week 4: 410-710 cells/μL), followed by a decline starting by week 24 to baseline or lower. No increases were seen in patients with eosinophilic esophagitis (baseline: 310 cells/μL; week 4: 230 cells/μL). In dupilumab-treated patients across all studies, rates of eosinophilia TEAEs were 0% to 13.6%. Clinical symptoms associated with increased eosinophils were rare (seven of 4,666 dupilumab-treated patients, including six cases of eosinophilic granulomatosis with polyangiitis) and occurred only in patients with asthma or CRSwNP. Eosinophilia was not associated with reduced dupilumab efficacy. CONCLUSIONS: Transient increases in eosinophil counts with dupilumab treatment did not affect efficacy and were rarely of clinical consequence. It remains important for physicians to base judgment on individual patient history and baseline eosinophil counts and to be alert to hypereosinophilic symptoms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 |
| 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".