Comparative Molecular Analysis of IL-17A and IL-23 Pathway Inhibition in Moderate-to-Severe Psoriasis: 4-Week Results from IXORA-R
Bibliographic record
Abstract
Ixekizumab (IXE), an IL-17A antagonist, and guselkumab (GUS), an IL-23p19 antagonist, are common psoriasis treatments. This longitudinal analysis assessed gene expression profiles in IXE- and GUS-treated patients with plaque psoriasis through week 4. In IXORA-R (NCT03573323), a head-to-head phase 4 study, adults with moderate-to-severe plaque psoriasis were assigned 1:1 to receive IXE or GUS. RNA expression was assessed in lesional tissue (n=72) from IXE-treated patients, GUS-treated patients, and healthy control groups (199 samples in total). Empirical Bayes was used to model RNA-sequencing data; differential expression was analyzed by tissue type, treatment, and time point, correcting for random effects. At week 1, lesions from IXE-treated patients had greater numbers of differentially expressed genes (392 upregulated, 696 downregulated) than those from GUS-treated patients (0 upregulated 0 downregulated). By week 4, the numbers of differentially expressed genes increased in both groups (IXE: 1882 upregulated, 1649 downregulated; GUS: 318 upregulated, 131 downregulated). Molecular shifts from baseline to week 4 occurred earlier with greater magnitude in IXE- than in GUS-treated patients. Rapid normalization of transcriptomic changes in patients receiving an IL-17A antagonist reflected downregulation of key inflammatory genes in psoriatic epidermis. Differentially expressed genes involved in epidermal IL-17A and IL-36 responses correlated with PASI 100 response.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".