Targeting Interleukin 13 for the Treatment of Atopic Dermatitis
Bibliographic record
Abstract
Atopic dermatitis (AD) is a common chronic inflammatory skin condition that has a significant impact on a patient's quality of life and requires ongoing management. Conventional topical and systemic therapies do not target specific components of AD pathogenesis and, therefore, have limited efficacy and may be associated with long-term toxicity. Thus, AD management is challenging, with a significant proportion of patients not achieving clear skin or a reduction in pruritus. There remains a large unmet need for effective therapeutic strategies with favorable safety profiles that can be used long-term in patients with refractory AD. The emergence of targeted biological and small molecule therapies has effectively broadened available treatment options for moderate-to-severe AD. Most recently, interleukin 13 (IL-13) inhibitors were shown to be efficacious and well-tolerated, with tralokinumab already approved for use in this patient population. It is important for dermatologists to be aware of the evidence behind this emerging class of biologic agents to guide treatment choices and improve outcomes in patients with AD. The main objective of this paper is to review the current literature regarding the efficacy and safety of current and emerging anti-IL-13 monoclonal antibodies, including tralokinumab, lebrikizumab, cendakimab, and eblasakimab, for the treatment of moderate-to-severe AD.
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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 | 0.000 |
| 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 | 0.000 | 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".