Article Sidebar

Submitted
Mar 13, 2021
Published
Nov 1, 2021
Download
Pdf
Statistic
Read Counter : 1029 Download : 4409

Downloads

Download data is not yet available.

Main Article Content

Abstract





The 3-(benzylideneamino)-2-(2,4-dichlorophenyl)-quinazoline-4(3H)-ones (BDCQ) are compounds developed as anticancer drugs and quinazolines. The activity and bioavailability of BDCQ derivatives as anticancer compounds that inhibit COX-2 can be predicted by computer programs and online servers. Substituents are added at positions 2 and 3 to the quinazoline-4(3H)-on ring, such as -H, -NO2, -OCH3, -N(CH3)2, -SO2NH2, -OH, and –OCH3. QSAR as COX-2 inhibitor analysis was performed by SPSS Ver. 21 software. Lipinski’s rule of five for determining bioavailability is performed by an online server at http://ilab.acdlabs.com. The best QSAR equation used to predict the COX-2 inhibitors from these compounds is RS-pred = 0.372 Log P + 0.014 MR + 0.979 Etot – 4.859, with n= 12, R = 0.998; SE = 0.356, F = 805.252 and sig = 0.001. Six compounds were predicted to have good oral bioavailability, such as 3-(benzylideneamino)-2-(2,4-dichlorophenyl)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((2-nitrobenzylidene)amino)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((3-nitrobenzylidene)amino)quinazoline-4(3H)-one,  2-(2,4-dichlorophenyl)-3-((2-methoxybenzilidene)amino)quinazoline-4(3H)-one, 2-(2,4-dichlorophenyl)-3-((3-methoxybenzylidene)amino)quinazolin-4(3H)-one,  and 2-(((2-(2,4-dichlorophenyl)-4-oxoquinazolin-3(4H)-yl)imino)methyl)- benzenesulfonamide. This research can be used as an in vitro and in vivo study for BDCQ derivatives as anticancer drugs.





 


Keywords

COX-2 inhibitor Lipinski’s rule of five QSAR Quinazolin-4(3H)-one

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Allaj, V., Guo, C., & Nie, D. (2013). Non-steroid anti-inflammatory drugs, prostaglandins, and cancer. Cell & bioscience, 3(1), 1-13.
  2. Blundell, T. L., Sibanda, B. L., Montalvão, R. W., Brewerton, S., Chelliah, V., Worth, C. L., Harmer, N. J., Davies, O., & Burke, D. (2006). Structural biology and bioinformatics in drug design: Opportunities and challenges for target identification and lead discovery. Philosophical Transactions of the Royal Society B: Biological Sciences, 361(1467), 413–423. https://doi.org/10.1098/rstb.2005.1800
  3. Bott, R. (2014). Data dan Informasi Kesehatan Situasi Penyakit Kanker. Igarss 2014, 1, 1–5. https://doi.org/10.1007/s13398-014-0173-7.2
  4. Chauhan, J. S., Dhanda, S. K., Singla, D., Agarwal, S. M., & Raghava, G. P. S. (2014). QSAR-based models for designing quinazoline/imidazothiazoles/ pyrazolopyrimidines based inhibitors against wild and mutant EGFR. PLoS ONE, 9(7). https://doi.org/10.1371/journal.pone.0101079
  5. Cnr, Q. C. N. R., & Date, R. (2014). Quinazolines Synthesis. June, 1–6.
  6. De Oliveira, C. S., Lira, B. F., Falcao-Silva, V. D. S., Siqueira-Junior, J. P., Barbosa-Filho, J. M., & De Athayde-Filho, P.
  7. F. (2012). Synthesis, molecular properties prediction, and anti-staphylococcal activity of N-acylhydrazones and new 1,3,4-oxadiazole derivatives. Molecules, 17(5), 5095–5107. https://doi.org/10.3390/molecules17055095
  8. Dong, J., Wang, N. N., Yao, Z. J., Zhang, L., Cheng, Y., Ouyang, D., Lu, A. P., & Cao, D. S. (2018). Admetlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics, 10(1), 1–11. https://doi.org/10.1186/s13321-018-0283-x
  9. El-Azab, A. S., Al-Omar, M. A., Abdel-Aziz, A. A. M., Abdel-Aziz, N. I., El-Sayed, M. A. A., Aleisa, A. M., Sayed-Ahmed, M. M., & Abdel-Hamide, S. G. (2010). Design, synthesis and biological evaluation of novel quinazoline derivatives as potential antitumor agents: Molecular docking study. European Journal of Medicinal Chemistry, 45(9), 4188–4198. https://doi.org/10.1016/j.ejmech.2010.06.013
  10. Ibrahim, M. T., Uzairu, A., Uba, S., & Shallangwa, G. A. (2020). Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors. Heliyon, 6(2), e03289. https://doi.org/10.1016/j.heliyon.2020.e03289
  11. Jiang, D., Lei, T., Wang, Z., Shen, C., Cao, D., & Hou, T. (2020). ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning. Journal of Cheminformatics, 12(1), 1–26. https://doi.org/10.1186/s13321-020-00421-y
  12. N. Noolvi, M., M. Patel, H., & Bhardwaj, V. (2011). A Comparative QSAR Analysis of Quinazoline Analogues as Tyrosine Kinase (erbB-2) Inhibitors. Medicinal Chemistry, 7(3), 200–212. https://doi.org/10.2174/157340611795564213
  13. Noolvi, M. N., & Patel, H. M. (2013). A comparative QSAR analysis and molecular docking studies of quinazoline derivatives as tyrosine kinase (EGFR) inhibitors: A rational approach to anticancer drug design. Journal of Saudi Chemical Society, 17(4), 361–379. https://doi.org/10.1016/j.jscs.2011.04.017
  14. Siswandono & Soekardjo B. (2000). Kimia Medisinal 1. Airlangga University Press.
  15. Sobolewski, C., Cerella, C., Dicato, M., Ghibelli, L., & Diederich, M. (2010). The Role of Cyclooxygenase-2 in Cell Proliferation and Cell Death in Human Malignancies. International Journal of Cell Biology, 2010, 1–21. https://doi.org/10.1155/2010/215158
  16. Widiyana, A.P., Putra, G.S., Muchlashi, & L. (2016). Design and Molecular Docking Studies of Quinazoline Derivatives as Antiproliferation. Jurnal Farmasi dan Ilmu Kefarmasian Indonesia Vol. 3 No. 1 Juli 2016 32. 3(1), 32–38.
  17. Zayed, M. F., Ahmed, H. E. A., Ihmaid, S., Omar, A. S. M., & Abdelrahim, A. S. (2015). Synthesis and screening of some new fluorinated quinazolinone-sulphonamide hybrids as anticancer agents. Journal of Taibah University Medical Sciences, 10(3), 333–339. https://doi.org/10.1016/j.jtumed.2015.02.007