Tricyclic coumarin sulphonate derivatives with alkaline phosphatase inhibitory effects: <i>in vitro</i> and docking studies
Bibliographic record
Abstract
Tissue-nonspecific alkaline phosphatase (TNAP) is an important isozyme of alkaline phosphatases, which plays different pivotal roles within the human body. Most importantly, it is responsible for maintaining the balanced ratio of phosphate and inorganic pyrophosphate, thus regulates the extracellular matrix calcification during bone formation and growth. The elevated level of TNAP has been linked to vascular calcification and end-stage renal diseases. Consequently, there is a need to search for highly potent and selective inhibitors of alkaline phosphatases (APs) for treatment of disorders associated with the over-expression of APs. Herein, a series of tricyclic coumarin sulphonate 1a-za with known antiproliferative activity, was evaluated for AP inhibition against human tissue nonspecific alkaline phosphatase (h-TNAP) and human intestinal alkaline phosphatase (h-IAP). The methylbenzenesulphonate derivative 1f (IC50 = 0.38 ± 0.01 μM) was found to be the most active h-TNAP inhibitor. Another 4-fluorobenzenesulphonate derivative 1i (IC50 = 0.45 ± 0.02 μM) was found as the strongest inhibitor of h-IAP. Some of the derivatives were also identified as highly selective inhibitors of APs. Detailed structure-activity relationship (SAR) was investigated to identify the functional groups responsible for the effective inhibition of AP isozymes. The study was also supported by the docking studies to rationalise the most possible binding site interactions of the identified inhibitors with the targeted enzymes.
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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 | 0.000 | 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) | 0.003 | 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".