Signal transducer and activator of transcription 3 inhibitors: a patent review
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Aberrant activation of signal transducer and activator of transcription (Stat) 3, a member of the STAT family of proteins, is prevalent in numerous human cancers and is now widely recognized as a critical molecular abnormality and a master regulator of tumor processes. Thus, the identification of potent and selective Stat3 inhibitors will have a high commercial potential as anticancer drugs, given the many tumors in which Stat3 is implicated. AREAS COVERED IN THIS REVIEW: This review covers the structures and activities of direct inhibitors of Stat3 protein activity described in the patent literature since the research field's inception in 2001. The patents reviewed include peptide and peptidomimetic compounds, small molecules, oligonucleotides and platinum-based Stat3 inhibitors. WHAT THE READER WILL GAIN: Readers will gain an understanding of how Stat3 protein function has been inhibited by a wide variety of structurally diverse therapeutic compounds. Readers will learn about which classes of patented Stat3 inhibitors are most advanced toward clinical trials, and will be exposed to the proposed mechanisms of inhibition and scope of their application in treating human cancers. TAKE HOME MESSAGE: Numerous groups have shown that in vivo administration of inhibitors of activated Stat3 induce human tumor regression in xenograft models. Indeed, the growing number of preclinical studies in numerous cancer types, as well as the first Phase 0 clinical trial of a Stat3 inhibitor, suggest that Stat3 is a valid and exciting therapeutic target for molecular inhibitors.
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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 | 0.002 | 0.002 |
| 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) | 0.009 | 0.006 |
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".