TYK 2 inhibitors for the treatment of dermatologic conditions: the evolution of JAK inhibitors
Bibliographic record
Abstract
Increasing understanding of cytokines as major drivers of immune-mediated diseases has revolutionized targeted treatments for these conditions. As the pathogenesis of autoimmune conditions is mediated by a complex interplay of various cytokines, Janus kinase (JAK) inhibitors have been of particular interest due to their ability to target multiple cytokines simultaneously. However, due to safety concerns with first generation JAK inhibitors, most notably from JAK2 and JAK3 inhibition, interest has shifted to more selective inhibition of TYK2. Three key TYK2 inhibitors that have advanced furthest in clinical trials for treatment of dermatologic autoimmune conditions are deucravacitinib (BMS-986165), brepocitinib (PF-06700841), and PF-06826647. This review outlines the current understanding of the efficacy and safety of these three TYK2 inhibitors from completed phase I and II studies and summarizes studies currently in progress for dermatologic conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".