T Helper 2 IL-4/IL-13 Dual Blockade with Dupilumab Is Linked to Some Emergent T Helper 17‒Type Diseases, Including Seronegative Arthritis and Enthesitis/Enthesopathy, but Not to Humoral Autoimmune Diseases
Bibliographic record
Abstract
Dupilumab, an IL-4/IL-13 receptor blocker, has been linked to emergent seronegative inflammatory arthritis and psoriasis that form part of the spondyloarthropathy spectrum. We systematically investigated patterns of immune disorders, including predominantly T helper 17‒(spondyloarthropathy pattern) and T helper 2‒mediated disorders and humoral autoimmune pattern diseases, using VigiBase, the World Health Organization's global pharmacovigilance of adverse drug reactions. Several bioinformatics databases and repositories were mined to couple dupilumab-related immunopharmacovigilance with molecular cascades relevant to reported findings. A total of 37,848 dupilumab adverse drug reaction cases were reported, with skin, eye, and musculoskeletal systems most affected. Seronegative arthritis (OR = 9.61), psoriasis (OR = 1.48), enthesitis/enthesopathy (OR = 12.65), and iridocyclitis (OR = 3.77) were highly associated. However, ankylosing spondylitis and inflammatory bowel disease were not conclusively associated. Overall, classic polygenic humoral‒mediated autoimmune diseases such as rheumatoid arthritis and systemic lupus erythematosus were not associated with dupilumab use. Pathway analysis identified several biological pathways potentially involved in dupilumab‒associated adverse drug reactions, including the fibroblast GF receptor (in particular, FGFR2) pathway. MicroRNAs analysis revealed the potential involvement of hsa-miR-21-5p and hsa-miR-335-5p. In conclusion, IL-4/IL-13 blockers are not unexpectedly protective against humoral autoimmune diseases but dynamically skew immune responses toward some IL-23/IL-17 cytokine pathway‒related diseases. IL-4/13 axis also plays a role in homeostatic tissue repair and we noted evidence for a link with ocular and arterial pathology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".