MG-K10, a Long-Acting Anti-IL-4 Receptor Alpha Monoclonal Antibody in Adult Patients with Moderate-to-Severe Atopic Dermatitis
Bibliographic record
Abstract
Abstract: Background: There remains unmet need for therapies for atopic dermatitis (AD) with favorable symptom control but less frequent dosing. Objective: To evaluate the benefits and safety of MG-K10, a humanized interleukin-4 receptor alpha-targeting antibody, in moderate-to-severe AD. Methods: In this multi-center, double-blind, randomized, phase II trial, 163 moderate-to-severe AD patients were randomized to receive 16-week treatment with MG-K10 150 mg every 4 weeks (Q4W), 300 mg every 2 weeks (Q2W) (n = 41), 300 mg Q4W (n = 41), or placebo (n = 40). Primary endpoint was the change in Eczema Area and Severity Index (EASI) scores from baseline to week 16. Results: The mean differences in EASI score at week 16 for the 300 mg Q4W, 300 mg Q2W, and 150 mg Q4W groups were −38.83% (95% confidence interval [CI]: −56.47% to −21.20%, P < 0.001), −27.06% (95% CI: −44.73% to −9.38%, P = 0.003), and −16.00% (95% CI: −33.66% to 1.67%, P = 0.076) compared with placebo group. The proportion of participants achieving EASI-75 at week 16 was 79.5%, 66.7%, 53.8%, and 28.9% in the 300 mg Q4W, 300 mg Q2W, 150 mg Q4W, and placebo groups. The incidence of adverse events was comparable across groups. Conclusion: MG-K10 demonstrates potential as a long-acting therapeutic option for managing symptoms of moderate-to-severe AD, with favorable safety profile. The preliminary efficacy and safety supported further validation of 300 mg Q4W in phase III trial.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".