A STAT inhibitor patent review: progress since 2011
Bibliographic record
Abstract
INTRODUCTION: The clinical utility of effective direct STAT inhibitors, particularly STAT3 and STAT5, for treating cancer and other diseases is well studied and known. AREAS COVERED: This review will highlight the STAT inhibitor patent literature from 2011 to 2015 inclusive. Emphasis will be placed on inhibitors of the STAT3, STAT5a/b, and STAT1 proteins for cancer treatment. The review will, where suitably investigated, describe the mode and the site of inhibition, list indications that were evaluated, and rank the inhibitor's relative potency among compounds in the same class. The reader will gain an understanding of the diverse set of approaches, used both in academia and industry, to target STAT proteins. EXPERT OPINION: There is still much work to be done to directly target the STAT3 and STAT5 proteins. As yet, there is still no direct STAT3 inhibitor in the clinic. While the SH2 domain remains a popular target for therapeutic intervention, the DNA-binding domain and N-terminal region are now attracting attention as possible sites for inhibition. Multiple putative STAT3 and STAT5 inhibitors have now been patented across a broad spectrum of chemotypes, each with their own advantages and limitations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.009 | 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".