A Review of Dupilumab-Induced Adverse Events to Dermatologists and the Potential Pathogenesis in the Treatment of Atopic Dermatitis
Bibliographic record
Abstract
Dupilumab, a monoclonal antibody targeting interleukin-4 antibody, is approved for use in many type 2 inflammatory diseases, including atopic dermatitis. It is generally well tolerated with no need of routine laboratory monitoring. However, several adverse events have been reported during real-world practice and in pivotal trials. We conducted a systematic literature research of the PubMed, Medline, and Embase databases to identify articles recording the clinical manifestation and potential pathogenesis of these adverse events with interests (AEIs) to dermatologists. In total, 547 cases from 134 studies have developed 39 AEIs 1 day to 2.5 years after dupilumab treatment. The most common AEIs are facial and neck dermatitis (299 cases), psoriasis (70 cases), arthralgia (56 cases), alopecia (21 cases), cutaneous T cell lymphoma (19 cases), severe ocular diseases (19 cases), and drug eruption (6 cases). Most of the AEIs recorded in this review resolved or improved after dupilumab discontinuation or the addition of another treatment, whereas 3 of the cases died of severe AEI. The potential pathogenesis included T help type 1 (Th1)/T help type 2 (Th2) imbalance, Th2/T help type 17 (Th17) imbalance, immune reconstitution, hypersensitivity reaction, transient hypereosinophilia related, and Th1 suppression. Clinicians should be alert of these AEIs for timely diagnosis and appropriate treatment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".