Small Molecule STAT5-SH2 Domain Inhibitors Exhibit Potent Antileukemia Activity
Bibliographic record
Abstract
A growing body of evidence shows that Signal Transducer and Activator of Transcription 5 (STAT5) protein, a key member of the STAT family of signaling proteins, plays a pivotal role in the progression of many human cancers, including acute myeloid leukemia and prostate cancer. Unlike STAT3, where significant medicinal effort has been expended to identify potent direct inhibitors, Stat5 has been poorly investigated as a molecular therapeutic target. Thus, in an effort to identify direct inhibitors of STAT5 protein, we conducted an in vitro screen of a focused library of SH2 domain binding salicylic acid-containing inhibitors (∼150) against STAT5, as well as against STAT3 and STAT1 proteins for SH2 domain selectivity. We herein report the identification of several potent (K(i) < 5 μM) and STAT5 selective (>3-fold specificity for STAT5 cf. STAT1 and STAT3) inhibitors, BP-1-107, BP-1-108, SF-1-087, and SF-1-088. Lead agents, evaluated in K562 and MV-4-11 human leukemia cells, showed potent induction of apoptosis (IC(50)'s ∼ 20 μM) which correlated with potent and selective suppression of STAT5 phosphorylation, as well as inhibition of STAT5 target genes cyclin D1, cyclin D2, C-MYC, and MCL-1. Moreover, lead agent BP-1-108 showed negligible cytotoxic effects in normal bone marrow cells not expressing activated STAT5 protein. Inhibitors identified in this study represent some of the most potent direct small molecule, nonphosphorylated inhibitors of STAT5 to date.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".