Fluoroquinolones as a potentially novel class of antidiabesity and antiproliferative compounds: synthesis and docking studies
Bibliographic record
Abstract
Intense efforts by the pharmaceutical industry have been made to identify new targets for obesity diabetes (diabesity). Pancreatic triacylglycerol lipase (PL) inhibition is an interesting putative target for obesity management. Fluoroquinolones (FQs) have been identified as potent inhibitors of PL. The aim of this research was to synthesize novel FQs and evaluate their in vitro antilipolytic and antiproliferative properties. Characterization of the synthesized FQs was carried out with NMR, MS, IR, and EA. Like orlistat, potential FQs’ modulation of PL was quantified colorimetrically (n = 3) and was further supported by docking studies. Compared with cisplatin, FQs’ antiproliferative propensities against a panel of obesity related colorectal cancer cell lines were investigated with Sulforhodamine B assay. Twelve novel FQs (2A–5A, 2B–5B, and 2C–5C) were synthesized and characterized. The PL-IC50 values of tested FQs were in the range of 6.8–165.7 μmol/L. FQ 4A was the most active antiproliferative compound against HCT116 with an IC50 value of 3.5 μmol/L. Their selectivity of growth inhibition for safety examination using normal periodontal ligament fibroblasts (PDL) in comparison with cisplatin’s lack of differential cytotoxicity was reported. Lipophilicity and hydrogen bonding were found essential for both activities. Conclusively, FQs are robustly proven for their emerging in vitro anti-obesity and antiproliferative activities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".