Unveiling the anticancer mechanism of 1,2,3-triazole-incorporated thiazole-pyrimidine-isoxazoles: insights from docking and molecular dynamics simulations
Bibliographic record
Abstract
Cancer is a major global health concern, and the constant search for novel, selective anticancer compounds with low toxicity is never ending. Nitrogen heterocyclic compounds such as pyrimidine and triazole have been identified as potential candidates for cancer treatment. A novel series of 1,2,3-triazole incorporated thiazole-pyrimidine-isoxazole derivatives 10 (a-j) were designed, synthesized and evaluated for antitumorigenic activities against human breast cancer (MCF-7), human lung cancer (A549) and human prostate (PC3 & DU-145) various cell-lines by employing MTT assay using etoposide as the positive control. The synthesized hybrids yielded decent efficacy, which was further compared with the standard drug. Among all the molecules, 10h revealed the more potent anticancerous activities, having IC50 values ranging from 0.011 ± 0.0017 µM; 0.063 ± 0.0012 µM; 0.017 ± 0.0094 µM and 0.66 ± 0.072 µM with DU145, PC3, A549, and MCF7 cell-lines, respectively. Tubulin, being a major protein involved with diverse biological actions, also serves, as a crucial target for several clinically practiced anticancer drugs, was utilized for docking analyses to evaluate the binding affinity of ligands. Docking results demonstrates that the selected hybrids 10 (g-j) exhibited good binding affinities with protein. Subsequently, drug likeness studies were carried out on the synthesized compounds to evaluate and analyze their drug like properties such as absorption, distribution, metabolism, excretion, and toxicity (ADMET) for toxicity prediction. Based on these analyses, the selected complexes were further employed for molecular dynamic simulations to analyze stability via an exhaustive cumulative 200 nanoseconds simulation. These results suggest that the selected compounds are stable and might serve as potential inhibitors to tubulin complex. In conclusion, we propose these synthesized compounds 10 (g-j) might provide new insights into cancer treatment and have potential for future development.Communicated by Ramaswamy H. Sarma
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".