Pursing Quinoline-8-Sulfonamide derivatives for the identification of potent NPPs inhibitors: In silico molecular docking, molecular dynamics simulations and density field theory (DFT) studies
Bibliographic record
Abstract
Nucleotide pyrophosphatase/phosphodiesterase (NPP) enzymes belong to a broad category of ectonucleotidase enzymes. These NPPs are responsible for hydrolysis of extracellular nucleotides (ATP to AMP) and are therefore important regulators in the purinergic cell signalling. Aberrant expression of NPP isozymes causes various diseases, including cancer, allergy and inflammation. In this study, a series of quinoline-8-sulfonamide derivatives (3a–3v) were synthesized and evaluated for their inhibitory potential against human NPP1 (h-NPP1) and h-NPP3. Most of the compounds exhibited significant activity against these enzymes. Notably, N-(2-fluoro-5-methylbenzyl)quinoline-8-sulfonamide (3d), N-(cyclohexylmethyl)quinoline-8-sulfonamide (3n), and N-(2-fluorophenyl)quinoline-8-sulfonamide (3q) were found to be potent inhibitors of h-NPP1, with IC50 values of 0.812 ± 0.071, 0.731 ± 0.013, 0.78 ± 0.07 μM, respectively. Additionally, N-(2,4-dichlorophenyl)quinoline-8-sulfonamide (3h) acted as a dual inhibitor of h-NPP1 (IC50 = 1.23 ± 0.011 μM) and h-NPP3 (IC50 = 0.871 ± 0.08 μM). To gain insight into the molecular interactions underlying inhibition, in silico molecular docking studies were conducted. The density functional theory (DFT) studies were also performed for geometry optimization and frequency calculation of the most potent quinoline-8-sulfonamide derivatives. Moreover, the frontier molecular orbital (FMO) analysis, molecular electrostatic potential (MEP) maps, as well as HOMO and LUMO orbitals were generated to better understand their electronic properties and reactivity profiles.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".