Targeting IL-22RA1 with temtokibart: A novel approach in atopic dermatitis: Phase 2a monotherapy study results
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease in which increased IL-22 expression contributes to epidermal hyperplasia and barrier defects. Temtokibart is a monoclonal antibody targeting IL-22RA1 (IL-22 receptor subunit alpha-1), blocking the signaling of IL-22 and potentially also of IL-20 and IL-24. OBJECTIVE: We evaluated the efficacy and safety of temtokibart in adults with moderate-to-severe AD. METHODS: In this phase 2a study (NCT04922021), 58 adults were randomized 1:1 to subcutaneous temtokibart 450 mg or placebo every 2 weeks for 16 weeks, with an additional dose (450 mg) at week 1, followed by additional 16 weeks of safety follow-up. The primary end point was change in Eczema Area Severity Index (EASI) from baseline to week 16. Biomarkers in serum, including IL-22, were analyzed as an exploratory end point. RESULTS: Mean change in EASI from baseline to week 16 was significantly greater for temtokibart compared to placebo (-15.3 vs -3.5; P = .003), corresponding to 65.4% and 19.7% improvement for temtokibart and placebo groups, respectively. At week 16, greater proportions of patients receiving temtokibart relative to placebo obtained EASI-75 (41.6% vs 13.7%; P = .011), EASI-90 (30.8% vs 3.5%; P = .003), and EASI-100 (20.9% vs 0%; P = .006). Treatment with temtokibart was well tolerated, and no safety signals were observed. Further, temtokibart treatment was associated with a general reduction of systemic inflammatory proteins. CONCLUSIONS: This proof-of-concept study demonstrates that targeting the IL-22 pathway with temtokibart is clinically effective with a favorable safety profile.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".