Selective Tyrosine Kinase 2 Inhibition for Treatment of Inflammatory Bowel Disease: New Hope on the Rise
Bibliographic record
Abstract
Conventional systemic and biologic agents are the mainstay of inflammatory bowel disease (IBD) management; however, many of these agents are associated with loss of clinical response, highlighting the need for effective, novel targeted therapies. Janus kinase (JAK) 1-3 and tyrosine kinase 2 (TYK2) mediate signal transduction events downstream of multiple cytokine receptors that regulate targeted gene transcription, including the interleukin-12, interleukin-23, and type I interferon receptors for TYK2. This review summarizes the role of TYK2 signaling in IBD pathogenesis, the differential selectivity of TYK2 inhibitors, and the potential clinical implications of TYK2 inhibition in IBD. A PubMed literature review was conducted to identify studies of JAK1-3 and TYK2 inhibitors in IBD and other immune-mediated inflammatory diseases. Key efficacy and safety information was extracted and summarized. Pan-JAK inhibitors provide inconsistent efficacy in patients with IBD and are associated with toxicities resulting from a lack of selectivity at therapeutic dosages. Selective inhibition of TYK2 signaling via an allosteric mechanism, with an agent that binds to the regulatory (pseudokinase) domain, may reduce potential toxicities typically associated with JAK1-3 inhibitors. Deucravacitinib, a novel, oral, selective TYK2 inhibitor, and brepocitinib and PF-06826647, TYK2 inhibitors that bind to the active site in the catalytic domain, are in development for IBD and other immune-mediated inflammatory diseases. Allosteric TYK2 inhibition is more selective than JAK1-3 inhibition and has the potential to limit toxicities typically associated with JAK1-3 inhibitors. Future studies will be important in establishing the role of selective, allosteric TYK2 inhibition in the management of IBD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".