5‐Fluoro/(trifluoromethoxy)‐2‐indolinone derivatives with anti‐interleukin‐1 activity
Bibliographic record
Abstract
Abstract The pro‐inflammatory cytokine interleukin‐1 (IL‐1) drives the pathogenesis of several inflammatory diseases. Recent studies have revealed that 2‐indolinones can modulate cytokine responses. Therefore, we screened several 2‐indolinone derivatives in preliminary studies to develop agents with anti‐IL‐1 activity. First, the putative efficacies and binding interactions of 2‐indolinones were evaluated by docking studies. Second, previously synthesized 5‐fluoro/(trifluoromethoxy)−1H‐indole‐2,3‐dione 3‐(4‐phenylthiosemicarbazones) (compounds 47–69) which had the highest inhibitory effect in the screening were evaluated for inhibitory effects on the IL‐1 receptor (IL‐1R). Compounds 52 (IC50 = 0.09 µM) and 65 (IC50 = 0.07 µM) were selected as lead compounds for the subsequent synthesis of new derivatives. The novel 5‐fluoro/(trifluoromethoxy)−1H‐indole‐2,3‐dione 3‐(4‐phenylthiosemicarbazones) (compounds 70–116) were designed, synthesized, and in vitro studies were completed. The compounds 76, 78, 81, 91, 100, 105, and 107 tested showed nontoxic inhibitory effects on IL‐1R‐dependent responses in the range of 0.01–0.06 µM and stronger than the lead compounds 52 and 65. In vitro and in silico findings showed that compounds 78 (IC50 = 0.01 µM) and 81 (IC50 = 0.02 µM) had the strongest IL‐1R inhibitory effects and the most favorable drug‐like properties. Molecular modeling studies of the compounds 78 and 81 were carried out to determine the possible binding interactions at the active site of the IL‐1R.
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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) | 0.000 | 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) | 0.002 | 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".