Differential Pharmacodynamic Effects on Psoriatic Biomarkers by Guselkumab Versus Secukinumab Correlate with Long-Term Efficacy: An ECLIPSE Substudy
Bibliographic record
Abstract
IL-23 is a cytokine produced by myeloid cells that drives the T helper 17 pathway and plays an essential role in the pathophysiology of plaque psoriasis. IL-23 activation initiates a cascade of cytokines subsequently inducing the expression of many psoriasis-related proteins. This study aimed to better understand the underlying mechanisms driving the differences between IL-23 and IL-17A blockade in patients with psoriasis and their implications for durability of clinical responses. Serum and/or skin biopsies were isolated from patients treated with guselkumab or secukinumab for evaluation of potential biomarkers of pharmacodynamic response to treatment. Guselkumab treatment led to significantly greater reductions of IL-17F and IL-22 serum levels than treatment with secukinumab at weeks 24 and 48, demonstrating sustained regulation of the IL-23/T helper 17 pathway. Analyses of proteomic and transcriptomic profiles of patient sera and skin biopsies demonstrated differential regulation of proteins involved in chemokine, TNF, and relevant immune signaling pathways to a greater degree with guselkumab than with secukinumab treatment. These data provide insights into the differences between the mechanisms and impact of IL-23 and IL-17A blockade in psoriasis, with implications for efficacy observations and treatment paradigms. Trial Registration: The original study was registered at ClinicalTrials.gov (NCT03090100).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".