159
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Discovery of anti-colon cancer agents targeting wild-type and mutant p53 using computer-aided drug design

ORCID Icon &
Pages 10171-10189 | Received 06 Jul 2022, Accepted 25 Nov 2022, Published online: 19 Dec 2022

References

  • Beura, S., & Chetti, P. (2021). In-silico strategies for probing chloroquine based inhibitors against SARS-CoV-2. Journal of Biomolecular Structure & Dynamics, 39(10), 3747–3759. https://doi.org/10.1080/07391102.2020.1772111
  • BIOvIA, D. S. (2015). Discovery studio modeling environment. San Diego, Dassault Systemes, Release, 4 https://doi.org/10.11436/mssj.17.98
  • Cancer. (n.d.). https://www.who.int/news-room/fact-sheets/detail/cancer
  • Chen, Y., Dey, R., & Chen, L. (2010). Crystal Structure of the p53 core domain bound to a full consensus site as a self-assembled tetramer. Structure (London, England : 1993), 18(2), 246–256. https://doi.org/10.1016/J.STR.2009.11.011
  • Cho, K. R., & Vogelstein, B. (1992). Suppressor gene alterations in the colorectal adenoma-carcinoma sequence. Journal of Cellular Biochemistry. Supplement, 16G(S16G), 137–141. https://doi.org/10.1002/JCB.240501124
  • Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. https://doi.org/10.1038/srep42717
  • Desai, N. C., Patel, B. Y., & Dave, B. P. (2017). Synthesis and antimicrobial activity of novel quinoline derivatives bearing pyrazoline and pyridine analogues. Medicinal Chemistry Research, 2016 26:126(1), 109–119. https://doi.org/10.1007/s00044-016-1732-6
  • Díaz Flaqué, M. C., Cayrol, M. F., Sterle, H. A., del Rosario Aschero, M., Díaz Albuja, J. A., Isse, B., Farías, R. N., Cerchietti, L., Rosemblit, C., & Cremaschi, G. A. (2019). Thyroid hormones induce doxorubicin chemosensitivity through enzymes involved in chemotherapy metabolism in lymphoma T cells. Oncotarget, 10(32), 3051–3065. https://doi.org/10.18632/ONCOTARGET.26890
  • Dorababu, A. (2021). Recent update on antibacterial and antifungal activity of quinoline scaffolds. Archiv Der Pharmazie, 354(3), 2000232. https://doi.org/10.1002/ardp.202000232
  • Dridi, W., Krabchi, K., Gadji, M., Lavoie, J., Bronsard, M., Fetni, R., Drouin, R., Dridi, W., Krabchi, K., Gadji, M., Lavoie, J., Bronsard, M., & Drouin, R. (2006). Activité dominante négative des protéines p53 mutées. Medecine Sciences : M/S, 22(3), 301–307. https://doi.org/10.1051/MEDSCI/2006223301
  • Eldar, A., Rozenberg, H., Diskin-Posner, Y., Rohs, R., & Shakked, Z. (2013). Structural studies of p53 inactivation by DNA-contact mutations and its rescue by suppressor mutations via alternative protein-DNA interactions. Nucleic Acids Research, 41(18), 8748–8759. https://doi.org/10.1093/nar/gkt630
  • Ferreira, L. L. G., & Andricopulo, A. D. (2019). ADMET modeling approaches in drug discovery. Drug Discovery Today, 24(5), 1157–1165. https://doi.org/10.1016/j.drudis.2019.03.015
  • Golbraikh, A., & Tropsha, A. (2002). Beware of q2!. Journal of Molecular Graphics & Modelling, 20(4), 269–276. https://doi.org/10.1016/S1093-3263(01)00123-1
  • Granados-Romero, J. J., Valderrama-Treviño, A. I., Contreras-Flores, E. H., Barrera-Mera, B., Enríquez, M. H., Uriarte-Ruíz, K., Ceballos-Villalba, J. C., Estrada-Mata, A. G., Rodríguez, C. A., & Arauz-Peña, G. (2017). Colorectal cancer: A review. International Journal of Research in Medical Sciences, 5(11), 4667–4676. https://doi.org/10.18203/2320-6012.ijrms20174914
  • Hadni, H., Bakhouch, M., & Elhallaoui, M. (2021). 3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors. Journal of Biomolecular Structure and Dynamics, 1–15. https://doi.org/10.1080/07391102.2021.2004233
  • Hadni, h., & Elhallaoui, m (2020). 2D and 3D-QSAR, molecular docking and ADMET properties in silico studies of azaaurones as antimalarial agents. New Journal of Chemistry, 44(16), 6553–6565. https://doi.org/10.1039/C9NJ05767F
  • Hadni, H., & Elhallaoui, M. (2020). 3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents. Heliyon, 6(4), e03580. https://doi.org/10.1016/j.heliyon.2020.e03580
  • Hadni, H., Fitri, A., Benjelloun, A. T., Benzakour, M., & Mcharfi, M. (2022). Evaluation of flavonoids as potential inhibitors of the SARS-CoV-2 main protease and spike RBD: Molecular docking, ADMET evaluation and molecular dynamics simulations. Journal of the Indian Chemical Society, 99(10), 100697. https://doi.org/10.1016/j.jics.2022.100697
  • Hadni, H., Mazigh, M., Charif, E., Bouayad, A., & Elhallaoui, M. (2018). Molecular modeling of antimalarial agents by 3D-QSAR study and molecular docking of two hybrids 4-Aminoquinoline-1,3,5-triazine and 4-Aminoquinoline-oxalamide derivatives with the receptor protein in its both wild and mutant types. Biochemistry Research International, 2018, 1–15. https://doi.org/10.1155/2018/8639173
  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/0263-7855(96)00018-5
  • Im, W., Seefeld, S., & Roux, B. (2000). A grand canonical Monte Carlo–Brownian dynamics algorithm for simulating ion channels. Biophysical Journal, 79(2), 788–801. https://doi.org/10.1016/S0006-3495(00)76336-3
  • Jia, C. Y., Li, J. Y., Hao, G. F., & Yang, G. F. (2020). A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discovery Today, 25(1), 248–258. https://doi.org/10.1016/j.drudis.2019.10.014
  • Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008). CHARMM-GUI: A web-based graphical user interface for CHARMM. Journal of Computational Chemistry, 29(11), 1859–1865. https://doi.org/10.1002/JCC.20945
  • Kar, S., Roy, K., & Leszczynski, J. (2018). Applicability domain: A step toward confident predictions and decidability for QSAR modeling. Methods in Molecular Biology (Clifton, N.J.), 1800, 141–169. https://doi.org/10.1007/978-1-4939-7899-1_6
  • Khan, K. M., Saify, Z. S., Khan, Z. A., Ahmed, M., Saeed, M., Schick, M., Kohlbau, H. J., & Voelter, W. (2000). Syntheses and cytotoxic, antimicrobial, antifungal and cardiovascular activity of new quinoline derivatives. Arzneimittel-Forschung, 50(10), 915–924. https://doi.org/10.1055/S-0031-1300313/BIB
  • Klebe, G., Abraham, U., & Mietzner, T. (1994). Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. Journal of Medicinal Chemistry, 37(24), 4130–4146. https://doi.org/10.1021/jm00050a010
  • Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., Donini, O., Cieplak, P., Srinivasan, J., Case, D. A., & Cheatham, T. E. (2000). Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Accounts of Chemical Research, 33(12), 889–897. https://doi.org/10.1021/AR000033J
  • Lu, H., Chen, C. S., & Waxman, D. J. (2009). Potentiation of methoxymorpholinyl doxorubicin antitumor activity by P450 3A4 gene transfer. Cancer Gene Therapy, 2009 16:5, 16(5), 393–404. https://doi.org/10.1038/cgt.2008.93
  • Mahamoud, A., Chevalier, J., Davin-Regli, A., Barbe, J., & Pages, J.-M. (2006). Quinoline derivatives as promising inhibitors of antibiotic efflux pump in multidrug resistant enterobacter aerogenes isolates. Current Drug Targets, 7(7), 843–847. https://doi.org/10.2174/138945006777709557
  • Michnová, H., Pospíšilová, Š., Spaczynska, E., Cieslik, W., Čížek, A., Musiol, R., & Jampílek, J. (2018). Antibacterial and Antifungal Activity of Styrylquinoline Derivatives, 5588 https://doi.org/10.3390/ECMC-4-05588
  • Mittal, R. R., Harris, L., McKinnon, R. A., & Sorich, M. J. (2009). Partial charge calculation method affects CoMFA QSAR prediction accuracy. Journal of Chemical Information and Modeling, 49(3), 704–709. https://doi.org/10.1021/ci800390m
  • Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., & Olson, A. J. (1998). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry, 19(14), 1639–1662. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B
  • Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Mrozek-Wilczkiewicz, A., Kuczak, M., Malarz, K., Cieślik, W., Spaczyńska, E., & Musiol, R. (2019). The synthesis and anticancer activity of 2-styrylquinoline derivatives. A p53 independent mechanism of action. European Journal of Medicinal Chemistry, 177, 338–349. https://doi.org/10.1016/J.EJMECH.2019.05.061
  • Musiol, R. (2020). Styrylquinoline - A Versatile Scaffold in medicinal chemistry. Medicinal Chemistry (Shariqah (United Arab Emirates)), 16(2), 141–154. https://doi.org/10.2174/1573406415666190603103012
  • Netzeva, T. I., Worth, A., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D., Schultz, T., Stanton, D. W., van de Sandt, J. J. M., Tong, W., Veith, G., & Yang, C. (2005). Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. Alternatives to Laboratory Animals : ATLA, 33(2), 155–173. https://doi.org/10.1177/026119290503300209
  • Onufriev, A., Bashford, D., & Case, D. A. (2004). Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins, 55(2), 383–394. https://doi.org/10.1002/PROT.20033
  • Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26(16), 1781–1802. https://doi.org/10.1002/JCC.20289
  • Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
  • Rivlin, N., Brosh, R., Oren, M., & Rotter, V. (2011). Mutations in the p53 tumor suppressor gene: Important milestones at the various steps of tumorigenesis. Genes & Cancer, 2(4), 466–474. https://doi.org/10.1177/1947601911408889
  • Roy, K. (2007). On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opinion on Drug Discovery, 2(12), 1567–1577. https://doi.org/10.1517/17460441.2.12.1567
  • Roy, K., & Mitra, I. (2011). On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Combinatorial Chemistry & High Throughput Screening, 14(6), 450–474. https://doi.org/10.2174/138620711795767893
  • Sarvagalla, S., Syed, S. B., & Coumar, M. S. (2019). An overview of computational methods, tools, servers, and databases for drug repurposing. In Silico Drug Design (pp. 743–780). Elsevier. https://doi.org/10.1016/b978-0-12-816125-8.00025-0
  • Soussi, T., & Lozano, G. (2005). p53 mutation heterogeneity in cancer. Biochemical and Biophysical Research Communications, 331(3), 834–842. https://doi.org/10.1016/J.BBRC.2005.03.190
  • Weiser, J., S., Shenkin, P., & Still, W. C. (1999). Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). Journal of Computational Chemistry, 20(2), 217–230. https://doi.org/10.1002/(SICI)1096-987X(19990130)20:2<217::AID-JCC4>3.0.CO;2-A
  • Wold, S., Ruhe, A., Wold, H., & Dunn, W. J. III, (1984). The collinearity problem in linear regression. The Partial Least Squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing, 5(3), 735–743. https://doi.org/10.1137/0905052
  • Yang, Y., Bin, Y. D., Qin, Q. P., Luo, X. J., Zou, B. Q., & Zhang, H. X. (2019). Novel quinoline-based Ir(III) complexes exhibit high antitumor activity in vitro and in vivo. ACS Medicinal Chemistry Letters, 10(12), 1614–1619. https://doi.org/10.1021/ACSMEDCHEMLETT.9B00337/SUPPL_FILE/ML9B00337_SI_002.PDF

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.