Tailoring Abrocitinib Treatment for Moderate-to-Severe Atopic Dermatitis to Patient Disease Course: A Narrative Review
Bibliographic record
Abstract
Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by intense itching, redness, and eczema. It significantly impacts the quality of life of affected individuals, often requiring long-term management strategies. Abrocitinib, an oral Janus kinase 1 (JAK1) inhibitor, is approved for the treatment of moderate-to-severe AD. Phase 2 and phase 3 abrocitinib randomized clinical trials in the JAK1 Atopic Dermatitis Efficacy and Safety (JADE) clinical development program have demonstrated the efficacy and safety of abrocitinib in both adults and adolescents with moderate-to-severe AD. This review article explores the benefit-risk profile of a flexible abrocitinib dosing approach, tailoring dose based on individualized treatment of patients and highlighting the available supportive data from the JADE randomized clinical trials for healthcare professionals as part of joint provider-patient decision making. Dosing flexibility and maintenance with the lowest effective dose is necessary to treat patients according to their individual disease course while minimizing safety risks. Safety data indicate that incidence of treatment-emergent adverse events is reflective of the current dosage, with no carry-over risk from a previous higher dosage. Overall, abrocitinib represents a valuable AD therapy that can be administered according to individual patient needs.Graphical abstract available for this article.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".