Design, synthesis, and biological evaluation of novel quinoxaline aryl ethers as anticancer agents.
Srinuvasu NakkaAsif RazaKosana Sai ChaitanyaNaga Venkata Madhusudhan Rao BandaruAla ChanduSankaranarayanan MurugesanNagaraju DevunuriArun K SharmaKondapalli Venkata Gowri Chandra SekharPublished in: Chemical biology & drug design (2024)
We designed and synthesized thirty novel quinoxaline aryl ethers as anticancer agents, and the structures of final compounds were confirmed with various analytical techniques like Mass, 1 H NMR, 13 C NMR, FTIR, and elemental analyses. The compounds were tested against three cancer cell lines: colon cancer (HCT-116), breast cancer (MDA-MB-231), prostate cancer (DU-145), and one normal cell line: human embryonic kidney cell line (HEK-293). The obtained results indicate that two compounds, FQ and MQ, with IC 50 values < 16 μM, were the most active compounds. Molecular docking studies revealed the binding of FQ and MQ molecules in the active site of the c-Met kinase (PDB ID: 3F66, 1.40 Å). Furthermore, QikProp ADME prediction and the MDS analysis preserved those critical docking data of both compounds, FQ and MQ. Western blotting was used to confirm the impact of the compounds FQ and MQ on the inhibition of the c-Met kinase receptor. The apoptosis assays were performed to investigate the mechanism of cell death for the most active compounds, FQ and MQ. The Annexin V/7-AAD assay indicated apoptosis in MDA-MB-231 cells treated with FQ and MQ, with FQ (21.4%) showing a higher efficacy in killing MDA-MB-231 cells than MQ (14.25%). The Caspase 3/7 7-AAD assay further supported these findings, revealing higher percentages of apoptotic cells for FQ-treated MDA-MB-231 cells (41.8%). The results obtained from the apoptosis assay conclude that FQ exhibits better anticancer activity against MDA-MB-231 cells than MQ.
Keyphrases
- cell cycle arrest
- cell death
- pi k akt
- induced apoptosis
- prostate cancer
- endoplasmic reticulum stress
- molecular docking
- high throughput
- high resolution
- squamous cell carcinoma
- tyrosine kinase
- endothelial cells
- molecular dynamics simulations
- machine learning
- big data
- transcription factor
- deep learning
- lymph node metastasis
- papillary thyroid
- single cell
- breast cancer risk