Clinical Implications of Targeting the JAK-STAT Pathway in Psoriatic Disease: Emphasis on the TYK2 Pathway
Bibliographic record
Abstract
Cytokines in the interleukin (IL)-23/IL-17 axis are central to psoriasis pathogenesis. Janus kinase (JAK) signal transducer and activator of transcription (STAT) regulates intracellular signalling of several cytokines (including IL-12, 23, 22, 6, 17, and interferon (IFN)-γ) in the IL-23/IL-17 axis, and, as a result, has become a therapeutic target for psoriasis treatment. Although several JAK1-3 inhibitors, with varying degrees of selectivity, have been developed for immune-mediated inflammatory diseases, use in psoriasis is limited by a low therapeutic index as anticipated by signals from other disease indications. More selective inhibition of the JAK family is an area of interest. Specifically, selective tyrosine kinase (TYK)2 inhibition suppresses IL-23/IL-17 axis signalling, and at therapeutic doses, has a favorable safety profile compared to therapeutic doses of JAK1-3 inhibitors. Phase III efficacy and safety data for the selective allosteric TYK2-inhibitor, deucravacitinib, in adult patients with moderate-to-severe plaque psoriasis is promising. Furthermore, phase II clinical trials for ropsacitinib (PF-06826647), a selective TYK2 inhibitor, and brepocitinib (PF-06700841), a JAK1/TYK2 inhibitor, have also demonstrated efficacy and an acceptable safety profile in adult patients with moderate-to-severe plaque psoriasis. Other novel TYK2 allosteric inhibitors, NDI-034858 and ESK-001, are currently being investigated in adult patients with plaque psoriasis. This article reviews the details of the JAK-STAT pathway in psoriasis pathophysiology, the rationale for selective targeting of JAKs in the treatment of psoriasis, and provides clinical perspective on clinical trial data for JAK and TYK2 inhibitors.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".