Synthesis and in-vitro anti-EGFR screening of new 1,2,3-triazole-benzimidazole hybrids and insilico studies
Bibliographic record
Abstract
Herein, the synthesis of benzimidazole- thiazolidine-2,4-dione -1,2,3-triazole conjugates (7a-7n) using copper (I) catalysed azide alkyne cycloaddition is reported. The synthesized compounds were screened for in vitro anticancer activity against MCF-7, MDA-MB-468 and MDA-MB-231 human breast cancer cells. Among all the compounds, four compounds namely 7d, 7i, 7k, and 7n displayed superior activity than 5-fluorouracil towards three breast cancer cell lines with IC50 values ranging from 1.8 mM to 9.7 mM. In vitro tyrosine kinase EGFR inhibition assay revealed that the compound 7d have 2.8 times more potency than that of erlotinib with IC50 value of 0.15mM and remaining three compounds (7i, 7k and 7n) also have more activity than erlotinib. Molecular docking studies on EGFR protein indicated that compound 7d exhibit greatest binding energy i.e. -11.04 kcal/mol compared to erlotinib. The molecule 7d was characterized by using density functional theory (DFT) with B3LYP/6–311++ G (d, p) basis set. The structural parameters were obtained from geometry optimization. Finally in silico pharmacokinetic profile also determined where 7d and 7i followed all the rules like Lipinski rule, Ghose rule, Veber rule, Egan rule and Muegge rule without any deviation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".