Lebrikizumab vs Other Systemic Monotherapies for Moderate to Severe Atopic Dermatitis: Network Meta-analysis of Short-term Efficacy
Bibliographic record
Abstract
Introduction: Treatment options for moderate-to-severe atopic dermatitis (AD) after inadequate response to topical therapy include biologics and Janus Kinase (JAK) inhibitors. A network meta-analysis (NMA) was conducted to evaluate the short-term efficacy of lebrikizumab relative to other monotherapies approved for moderate-to-severe AD in adults and adolescents (≥12 years). Methods: Data were included from randomized, double-blind, placebo-controlled monotherapy trials of lebrikizumab 250 mg every 2 weeks (Q2W), dupilumab 300 mg Q2W, tralokinumab 300 mg Q2W, abrocitinib 100 or 200 mg daily, baricitinib 2 or 4 mg daily, and upadacitinib 15 or 30 mg daily. Efficacy outcomes included the proportion of patients achieving Eczema Area and Severity Index (EASI) improvement, Investigator Global Assessment of 0 or 1 (IGA 0/1), and ≥4-point improvement in pruritus/itch numeric rating scale (NRS) score at 12 weeks (abrocitinib) or 16 weeks (other treatments). A Bayesian NMA was used to estimate number needed to treat (NNT), odds ratios (ORs), probabilities, and 95% credible intervals (CrI). Results: Twenty-two monotherapy studies involving 8531 patients were identified. For itch improvement, lebrikizumab had lower NNT values (2.90, 95% CrI: 2.26–3.80) than baricitinib 2 mg (11.12, 6.11–29.30) or 4 mg (8.10, 4.42–19.64) and tralokinumab (7.03, 4.81–11.17). Lebrikizumab had comparable NNT values for itch relative to abrocitinib 100 mg (3.50, 2.67–4.91) or 200 mg (2.31, 1.90–2.93), upadacitinib 15 mg (2.57, 2.09–3.29) or 30 mg (2.02, 1.74–2.46), and dupilumab (3.47, 2.78–4.58). NNT values for IGA 0,1 and EASI response were similar, except that upadacitinib 30 mg QD had lower NNT values than biologics at week 16. In pairwise comparisons, lebrikizumab was statistically superior to baricitinib and tralokinumab and comparable to abrocitinib, dupilumab, and upadacitinib 15 mg across all outcomes. Lebrikizumab had statistically inferior outcomes to upadacitinib 30 mg at week 16. Conclusion: Lebrikizumab has comparable or better short-term efficacy relative to other biologics and JAK inhibitors, except for upadacitinib 30 mg. Lebrikizumab may be a highly promising first-line biologic for moderate-to-severe AD, offering patients meaningful symptom relief.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.044 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".