Antiproliferative evaluations of triazoloquinazolines as classical DNA intercalators: Design, synthesis, ADMET profile, and molecular docking.
Ibrahim H EissaMohamed-Kamal IbrahimMohamed S AlesawyKhaled El-AdlPublished in: Archiv der Pharmazie (2022)
Novel triazoloquinazolines were designed and synthesized and evaluated as anticancer agents against HepG2 and HCT-116 cells. The biological testing data corresponded well to those of the molecular docking studies. The HCT-116 cell line was most affected due to the actions of our derivatives. Derivative 7 a was the most potent one against both HepG2 and HCT116 cells, with IC 50 = 7.98 and 5.57 µM, respectively. This compound showed anticancer activity that was nearly equipotent to that of doxorubicin against HepG2 cells, but higher than that of doxorubicin against HCT116 cells (IC 50 = 7.94 and 8.07 µM, respectively). Compounds 8, 7 b , and 6 f showed excellent anticancer activities against both the HCT116 and HepG2 cell lines. The highly active compounds 6 f , 7 a , 7 b , and 8 were evaluated for their DNA-binding activities. Compounds 7 a and 8 showed the highest binding activities. These derivatives potently intercalate in DNA, at IC 50 values of 42.90 and 48.13 µM, respectively. Derivatives 6 f and 7 b showed good DNA-binding activities, with IC 50 values of 54.24 and 50.56 µM, respectively. Furthermore, in silico calculated ADMET profiles were established for our four highly active derivatives, in comparison to doxorubicin. Our derivatives 6 f , 7 a , 7 b , and 8 showed a very good ADMET profile. Compounds 6 f , 7 a , 7 b , and 8 follow Lipinski's rules, while doxorubicin violates three of these rules.
Keyphrases
- molecular docking
- cell cycle arrest
- dna binding
- cell death
- induced apoptosis
- molecular dynamics simulations
- pi k akt
- drug delivery
- transcription factor
- cancer therapy
- structure activity relationship
- circulating tumor
- endoplasmic reticulum stress
- cell free
- single molecule
- electronic health record
- machine learning
- signaling pathway
- big data
- cell proliferation
- deep learning