Selective JAK1 inhibitors for the treatment of inflammatory bowel disease
Bibliographic record
Abstract
Janus kinase (JAK) inhibitors, also known as jakinibs, are third-generation oral small molecules that have expanded the therapeutic options for the management of chronic inflammatory diseases, including inflammatory bowel disease (IBD). Tofacitinib, a pan-JAK inhibitor, has spearheaded the new JAK class for IBD treatment. Unfortunately, serious adverse effects, including cardiovascular complications such as pulmonary embolism and venous thromboembolism or even death from any cause, have been reported for tofacitinib. However, it is anticipated that next-generation selective JAK inhibitors may limit the development of serious adverse events, leading to a safer treatment course with these novel targeted therapies. Nevertheless, although this drug class was recently introduced, following the launch of second-generation biologics in the late 1990s, it is breaking new ground and has been shown to efficiently modulate complex cytokine-driven inflammation in both preclinical models and human studies. Herein, we review the clinical opportunities for targeting JAK1 signaling in the pathophysiology of IBD, the biology and chemistry underpinning these target-selective compounds, and their mechanisms of actions. We also discuss the potential for these inhibitors in efforts to balance their benefits and harms.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".