Guselkumab Reduces Disease- and Mechanism-Related Biomarkers More Than Adalimumab in Patients with Psoriasis: A VOYAGE 1 Substudy
Bibliographic record
Abstract
Background Psoriasis is an immune-mediated inflammatory disease characterized by activation of interleukin (IL)-23–driven IL-17–producing T cell and other IL-23 receptor+ IL-17–producing cell responses. Selective blockade of IL-23p19 with guselkumab was superior to blockade of tumor necrosis factor-α (TNF-α) with adalimumab in treating moderate-to-severe psoriasis. Objective Pharmacodynamic (PD) responses of guselkumab versus adalimumab were compared in patients with psoriasis in VOYAGE 1. Design Inflammatory cytokine serum levels were assessed (n=118) and lesional and nonlesional skin biopsies were collected (n=38) in patient subsets at baseline, 4/24/48-weeks post-treatment to evaluate PD responses of guselkumab versus adalimumab. Results Guselkumab provided rapid reductions in serum IL-17A, IL-17F, and IL-22 levels by Week 4 versus baseline that were maintained through Weeks 24 and 48 (p<0.001). The magnitude of reduction of IL-17A and IL-22 at Week 48, and IL-17F at Weeks 4/24/48 were greater with guselkumab versus adalimumab (all p<0.05). In skin, guselkumab reduced expression of IL-23/IL-17 pathway-associated and psoriasis-associated genes. Conclusion These data provide extensive characterization of PD anti-inflammatory responses to IL-23p19 and TNF-α inhibition in human blood and tissue over time with clinically approved doses of guselkumab and adalimumab.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".