Effectiveness and tolerance of tralokinumab after failure with dupilumab and baricitinib in a patient with atopic dermatitis
Bibliographic record
Abstract
BackgroundTralokinumab is a human monoclonal anti-interleukin-13 antibody approved as systemic treatment for atopic dermatitis (AD).ObjectivesWe aimed to evaluate effectiveness and safety of tralokinumab for AD in real-world clinical practice.Materials & MethodsWe analysed Japanese patients with AD from October 2023 to March 2024. All patients were subcutaneously injected with tralokinumab, 300 mg every two weeks, after an initial injection of 600 mg and twice-daily topical corticosteroids of moderate to strongest class until week 12.ResultsIn this study, 103 patients were analysed. At week 4 and 12, 54.7 % and 83.0 % achieved eczema area and severity index (EASI) 50, 22.7 % and 38.3 % achieved EASI 75, 90 8.0 % and 23.4 % achieved EASI, 32.0 % and 55.3 % achieved EASI ≤ 7, and 1.3 % and 14.0 % achieved Investigator's Global Assessment 0/1, respectively. At week 4 and 12, 52.9 % and 51.2 % achieved Peak Pruritus-Numerical Rating Scale (PP-NRS) 4, 16.5 % and 15.6 % achieved PP-NRS ≤1, and 57.9 % and 75.0 % achieved Atopic Dermatitis Control Tool 7, respectively. Serum levels of immunoglobulin E, thymus and activation-regulated chemokine, and lactate dehydrogenase significantly decreased at week 12 compared to baseline. Treatment-emergent adverse events occurred in 14.8 % of patients, which were mild and manageable. Notably, conjunctivitis occurred in 2.9 % of patients but was mild and resolved spontaneously.ConclusionTralokinumab for patients with AD was well-tolerated and provided favourable therapeutic effects in real-world clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".