New series of 4,6-diaryl pyrimidines: facile synthesis and antiproliferative activity as dual EGFR/VEGFR-2 inhibitors
Bibliographic record
Abstract
Introduction We developed and produced a new series of 4,6-diaryl-pyrimidines 9–29 as antiproliferative agents targeting EGFR/VEGFR-2. Methods The antiproliferative efficacy of the novel targets was assessed against a panel of 60 NCI cancer cell lines and four cancer cell lines in vitro. Results and Discussion Compounds 14, 17, 19, 22, 25, and 29 demonstrated the greatest potency among the derivatives, with GI50 values between 22 and 33 nM; compounds 22 and 29 exhibited the highest potency, with GI50 values of 22 and 24 nM, respectively. We subsequently examined the most efficient derivatives as dual EGFR/VEGFR-2 inhibitors, finding that compounds 22 and 29 functioned as dual inhibitors. Moreover, 22 and 29 can act as apoptotic inducers by increasing Bax levels and decreasing levels of the anti-apoptotic protein Bcl2. At both 24- and 48-h intervals, the cell migration rates of compounds 22 and 29 were lower than those of untreated cells, according to the migration rate and wound closure percentage assessment. The wound closure rate reached 100% after 72 h of therapy with compound 22 but only 80% with compound 29. The docking study showed that compounds 22 and 29 had docking scores similar to those of Erlotinib and Sorafenib, co-crystallized ligands, for the EGFR and VEGFR-2 proteins. The experiments on lipophilicity showed that the new pyrimidines had a consistent result. This group of compounds has better biological activity in all the biological systems studied with low lipophilicity.
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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) | 0.000 | 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 | 0.001 |
| 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".