Amlitelimab Reduces Th2-, Th1-, and Th17/22-Related Cytokines and Chemokines in Adults With Moderate-to-Severe Atopic Dermatitis: Results From an Exploratory Analysis of the Phase 2b STREAM-AD Study
Bibliographic record
Abstract
Introduction Amlitelimab, a fully human, nondepleting monoclonal antibody, binds OX40 ligand (OX40L) on antigen-presenting cells, preventing OX40L-OX40 interaction on activated T cells. In adults with moderate-to-severe atopic dermatitis (AD), amlitelimab demonstrated clinically meaningful improvements in AD lesions and pruritus, and reduced AD-related plasma and cutaneous biomarkers over 24 weeks compared with placebo-treated patients in Part 1 of STREAM-AD. This analysis evaluates the effect of amlitelimab on AD-related cytokines and chemokines, including those associated with Th2, Th1, and Th17/Th22 inflammation. Methods STREAM-AD (NCT05131477) was a 52-week, Phase 2b trial with two parts: a 24-week dose-ranging phase and a 28-week maintenance phase. In Part 1, adults with moderate-to-severe AD were randomized to receive subcutaneous amlitelimab (250mg with 500-mg loading dose, 250mg, 125mg, 62.5mg) or placebo every 4 weeks. Here, changes in inflammatory proteins from baseline to weeks 4 and 16 were evaluated utilizing an exploratory protein multiplex panel (Olink® Explore 384 Inflammation I, Olink Proteomics) on plasma from patients with AD treated with amlitelimab (n=300) vs placebo (n=76) in Part 1. Results Amlitelimab reduced plasma proteins associated with Th2 inflammation, including CCL13, CCL17, CCL22, CCL26, IL-13, and IL-24 (P<0.01 for all) from baseline to Week 16. It also reduced markers of Th1-related inflammation, including CXCL9, CXCL10, and TNF (P<0.01 for all), and Th17/22-related inflammation, including CCL20, IL-17A, IL-17C (P<0.01 for all), and IL-6 (P<0.05). Conclusion Amlitelimab significantly reduced proteins associated with AD inflammation in adults with moderate-to-severe AD, further supporting that OX40L blockade is a relevant target for treating AD-related inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".