IL-13 inhibition in the treatment of atopic dermatitis – new and emerging biologic agents
Bibliographic record
Abstract
Atopic dermatitis (AD) is a common, chronic, and recurrent inflammatory skin condition that affects a considerable portion of the population, and is particularly prevalent among children. The development of AD is influenced by environmental and genetic factors, which cause epidermal barrier dysfunction, immune dysregulation, and dysbiosis. In immune dysregulation, there is excessive production of cytokines. Among the cytokines, interleukin (IL)-13 plays a major role in the pathogenesis of AD. Searching for new and more selective treatments for moderate-to-severe cases is important because of the considerable effect of AD on the quality of life. Tralokinumab and lebrikizumab are selective IL-13 inhibitors that have demonstrated safety and efficacy as treatment options for AD in phase III trials. Tralokinumab is approved for use in Europe and the USA, while lebrikizumab is approved only in Europe. Cendakimab, which is another IL-13 selective inhibitor, has shown promising results in phase II trials, providing safe and effective outcomes. Eblasakimab, which disrupts IL-13 and IL-4 signaling pathways, is currently in phase II trials following well-tolerated administration in phase I studies. This narrative review aims to outline the current state of knowledge regarding the effectiveness and safety of these four biologic agents targeting IL-13 signaling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".