Protein kinase inhibitors in the treatment of inflammatory and autoimmune diseases
Bibliographic record
Abstract
Protein kinases mediate protein phosphorylation, which is a fundamental component of cell signalling, with crucial roles in most signal transduction cascades: from controlling cell growth and proliferation to the initiation and regulation of immunological responses. Aberrant kinase activity is implicated in an increasing number of diseases, with more than 400 human diseases now linked either directly or indirectly to protein kinases. Protein kinases are therefore regarded as highly important drug targets, and are the subject of intensive research activity. The success of small molecule kinase inhibitors in the treatment of cancer, coupled with a greater understanding of inflammatory signalling cascades, has led to kinase inhibitors taking centre stage in the pursuit for new anti-inflammatory agents for the treatment of immune-mediated diseases. Herein we discuss the main classes of kinase inhibitors; namely Janus kinase (JAK), mitogen-activated protein kinase (MAPK) and spleen tyrosine kinase (Syk) inhibitors. We provide a mechanistic insight into how these inhibitors interfere with kinase signalling pathways and discuss the clinical successes and failures in the implementation of kinase-directed therapeutics in the context of inflammatory and autoimmune disorders.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".