Distinctive inhibition of alkaline phosphatase isozymes by thiazol‐2‐ylidene‐benzamide derivatives: Functional insights into their anticancer role
Bibliographic record
Abstract
Abstract In the recent years, the role of alkaline phosphatase (AP) isozymes in the cause of neoplastic diseases such as breast, liver, renal, and bone cancer has been confirmed and, thus they represent a novel target for the discovery of anticancer drugs. In this study different derivatives of thiazol‐2‐ylidene‐benzamide were evaluated for their potential to inhibit alkaline phosphatase (AP) isozymes. Their anticancer potential was assessed using human breast cancer (MCF‐7), bone‐marrow cancer (K‐562), and cervical cancer (HeLa) cell lines in comparison to normal cells from baby hamster kidney BHK‐21. The results suggested that in comparison to other derivatives, compounds 2i , 2e , and 2a showed more sensitivity towards human tissue non‐specific alkaline phosphatase ( h ‐TNAP). Among these, 2″‐chloro‐ N ‐(3‐(4′‐fluorophenyl)‐4‐methylthiazol‐2(3 H )‐ylidene) benzamide ( 2e ) was found as the most potent and selective inhibitor for h ‐TNAP with an IC 50 value of 0.079 ± 0.002 μM. Moreover, a significant correlation was observed between the enzyme inhibition profile and cytotoxic data. The compounds exhibiting maximum anticancer potential also induced maximum apoptosis in the respective cell lines. Furthermore, the DNA interaction studies exhibited the non‐covalent mode of interaction with the herring sperm‐DNA. Molecular docking studies also supported the in vitro inhibitory activity of potent compounds. Our findings suggested that potent and selective inhibitors might be useful candidates for the treatment or prevention of those diseases associated with the higher level of AP. Moreover, the study can be useful for the researcher to explore more molecular mechanisms of such derivatives and their analogues with the exact findings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".