Abstract C40: Salicylic acid-based small molecule inhibitors of Stat3 show potent and selective activity in a variety of human cancer cell lines
Bibliographic record
Abstract
Abstract Signal transducer and activator of transcription 3 (Stat3) protein is an oncogenic transcription factor that exhibits aberrant functioning in many types human cancer. Constitutive Stat3 activation causes over-expression of anti-apoptotic proteins and makes cancer cells resistant to natural apoptotic processes. Inhibiting Stat3 reduces the expression of anti-apoptotic proteins and can selectively kill cancerous cells. We have identified several salicylic acid based inhibitors of Stat3 that show potent activity in vitro and in whole cell assays. In silico docking studies have revealed S3I-201, a known Stat3 inhibitor, exhibits incomplete occupation of Stat3's SH2 domain. A thorough structure activity relationship study has provided a series of compounds that more completely occupy Stat3's SH2 domain and show potent and highly selective activity against Stat3 and a variety of human cancers. Inhibitor design, synthesis, in vitro and in vivo assays and preliminary metabolic studies will be discussed. Citation Information: Cancer Res 2009;69(23 Suppl):C40.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".