Disarming an Electrophilic Warhead: Retaining Potency in Tyrosine Kinase Inhibitor (TKI)‐Resistant CML Lines While Circumventing Pharmacokinetic Liabilities
Bibliographic record
Abstract
Pharmacologic blockade of the activation of signal transducer and activator of transcription 3 (STAT3) in tyrosine kinase inhibitor (TKI)-resistant chronic myeloid leukemia (CML) cell lines characterized by kinase-independent resistance was shown to re-sensitize CML cells to TKI therapy, suggesting that STAT3 inhibitors in combination with TKIs are an effective combinatorial therapeutic for the treatment of CML. Benzoic acid- and hydroxamic acid-based STAT3 inhibitors SH-4-054 and SH-5-007, developed previously in our laboratory, demonstrated promising activity against these resistant CML cell lines. However, pharmacokinetic studies in murine models (CD-1 mice) revealed that both SH-4-054 and SH-5-007 are susceptible to glutathione conjugation at the para position of the pentafluorophenyl group via nucleophilic aromatic substitution (SN Ar). To determine whether the electrophilicity of the pentafluorophenyl sulfonamide could be tempered, an in-depth structure-activity relationship (SAR) study of the SH-4-054 scaffold was conducted. These studies revealed that AM-1-124, possessing a 2,3,5,6-tetrafluorophenylsulfonamide group, retained STAT3 protein affinity (Ki =15 μm), as well as selectivity over STAT1 (Ki >250 μm). Moreover, in both hepatocytes and in in vivo pharmacokinetic studies (CD-1 mice), AM-1-124 was found to be dramatically more stable than SH-4-054 (t1/2 =1.42 h cf. 10 min, respectively). AM-1-124 is a promising STAT3-targeting inhibitor with demonstrated bioavailability, suitable for evaluation in preclinical cancer models.
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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".