Synthesis, Biological Evaluation and Molecular Docking Studies of Novel 4‐Propylsulfonylpiperazines‐Based Thiosemicarbazones as Ecto‐5′‐Nucleotidase and NTPDase Inhibitors
Bibliographic record
Abstract
Purinergic signaling is modulated by extracellular enzymes known as ectonucleotidases. Ecto-5'-nucleotidase and NTPDases are part of the ectonucleotidase family. NTPDases control ATP levels through hydrolysis, whereas ecto-5'-NT collaborates with NTPDase to break down nucleotide molecule. Due to their roles in inflammation, infection, and cancer, both enzymes present promising targets for therapeutic interventions. In this study, we present a novel and environment-friendly synthetic approach for the creation of small molecules that are not based on nucleotides, specifically substituted sulfonyl-piperazine-based thiosemicarbazone derivatives 7(a-s). We assessed their inhibitory effects on ecto-5'-nucleotidase and NTPDase1, 2, 3, and 8. Most of the compounds displayed excellent inhibition against one or more forms, while some displayed selective inhibition. To gain a deeper understanding of how the synthesized compounds interact with the isoenzymes, we conducted molecular docking studies. Additionally, ADME analyses were performed to predict the pharmacokinetic properties of these compounds. The integration of in vitro and in silico studies enabled the identification of compounds with potential inhibitory activity and favorable binding orientations. The observed results provide compelling evidence for the potency of the biologically active scaffold, sulfonylpiperazine, as a powerful and selective NTPDase inhibitor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".