In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line
Tóm tắt
Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 μM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range −10.8 to −11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.
Tài liệu tham khảo
Siegel RL, Miller KD, Jemal A (2020) Cancer statistics, 2020. A Cancer Journal for Clinicians, CA, pp 7–30 https://doi.org/10.3322/caac.21590
Kolligs FT (2016) Diagnostics and epidemiology of colorectal cancer. Visceral Medicine 32:158–164 https://doi.org/10.1159/000446488
Simon L, Imane A, Srinivasan KK, Pathak L, Daoud I (2016) In silico drug-designing studies on flavanoids as anticolon cancer agents: pharmacophore mapping, molecular docking, and Monte Carlo method-based QSAR modeling. Interdiscip Sci Comput Life Sci. https://doi.org/10.1007/s12539-016-0169-4
Araghi M, Soerjomataram I, Jenkins M, Brierley J, Morris E, Bray F, Arnold M (2019) Global trends in colorectal cancer mortality: projections to the year 2035. Int J Cancer 144(12):2992–3000 https://doi.org/10.1002/ijc.32055
Qawoogha, S. S. and Shashiwala, A. (2020). Identification of potential anticancer phytochemicals against colorectal cancer by structure-based docking studies. Journal of Receptors and Signal Transduction¸ https://doi.org/10.1080/10799893.2020.1715431
Bekkink MO, McCowan C, Falk GA, Teljeur C, Van de Laar FA, Fahey T (2010) Diagnostic accuracy systematic review of rectal bleeding in combination with other symptoms, signs and tests in relation to colorectal cancer. Br J Cancer 102:48–58 https://doi.org/10.1038/sj.bjc.6605426
Mármol I, Sánchez-de-Diego C, Dieste A, Cerrada E, Yoldi R (2017) Colorectal carcinoma: a general overview and future perspectives in colorectal cancer. Int J Mol Sci 18:1 https://doi.org/10.3390/ijms18010197
Clarke JM, Hurwitz HI (2013) Targeted inhibition of VEGF receptor 2: an update on ramucirumab. Expert Opin Biol Ther 13(8):1187–1196 https://doi.org/10.1517/14712598.2013.810717
Shi X, Li H, Yao H, Liu X, Li L, Leung K, Kung H, Lin M (2015) Adapalene inhibits the activity of cyclin-dependent kinase 2 in colorectal carcinoma. Mol Med Rep 12:6501–6508 https://doi.org/10.3892/mmr.2015.4310
Tadesse S, Anshabo AT, Portman N, Lim E, Tilley W, Caldon E, Wang S (2019) Targeting CDK2 in cancer: challenges and opportunities for therapy. Drug Discov Today https://doi.org/10.1016/j.drudis.2019.12.001
Wood DJ, Korolchuk S, Tatum NJ, Wang LZ, Endicott JA, Noble ME, Martin MP (2018) Differences in the conformational energy landscape of CDK1 and CDK2 suggest a mechanism for achieving selective CDK inhibition. Cell Chemical Biology 26(1):121–130 https://dx.doi.org/10.1016%2Fj.chembiol.2018.10.015
Cicenas J, Kalyan K, Sorokinas A, Stankunas E, Levy J et al (2015) Roscovitine in cancer and other diseases. Annals of Translational Medicine 3(10) https://dx.doi.org/10.3978%2Fj.issn.2305-5839.2015.03.61
Dachineni R, Ai G, Kumar R, Sadhu S, Tummala H, Bhat J (2015) Cyclin A2 and CDK2 as novel targets of aspirin and salicylic acid: a potential role in cancer prevention. Mol Cancer Res 14(3):241–252 https://doi.org/10.1158/1541-7786.MCR-15-0360
Oh, S. J., Erb, H. H., Hobisch, A., Santer, F. R. and Culig, Z. (2012). Endocrine-related cancer, 19, 305 – 319. https://doi.org/10.1530/ERC-11-0298
Tadesse S, Caldon E, Tilley W, Wang S (2018) Cyclin dependent kinase 2 inhibitors in cancer therapy: an update. J Med Chem https://doi.org/10.1021/acs.jmedchem.8b01469
Abo-Elanwar Y, Mostafa AS, El-Sayed MA, Nasr MN (2019) Synthesis and biological evaluation of new 2-(4-fluorophenyl) imidazol-5-ones as anticancer agents. Journal of Applied Pharmaceutical Science 9(05):1–11 https://doi.org/10.7324/JAPS.2019.90501
Adeniji, S. E., Uba, S., Uzairu, A. and Arthur, D. E. (2019). A derived QSAR model for predicting some compounds as potent antagonist against Mycobacterium tuberculosis: a theoretical approach. Hindawi, https://doi.org/10.1155/2019/5173786
Abdullahi M, Uzairu A, Shallangwa GA, Mamza P, Arthur DE, Ibrahim MT (2019) An Insilico modelling study on some C14-urea-tetrandrine derivatives as potent anti-cancer against prostate (PC3) cell line. Journal of King Saud University – Science https://doi.org/10.1016/j.jksus.2019.01.008
Becke AD (1993) Becke’s three parameter hybrid method using the LYP correlation functional. J Chem Phys 98:5648–5652
Ibrahim, M. T., Uzairu, A., Shallangwa, G. A. and Ibrahim, A. (2018). In-silico studies of some oxadiazoles derivatives as anti-diabetic compounds. Journal of King Saud University –Science. https://doi.org/10.1016/j.jksus.2018.06.006.
Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148
Ikwu FA, Shallangwa GA, Mamaza AP, Uzairu A (2020) In silico studies of piperazine derivatives as potent anti-proliferative agents against PC-3 prostate cancer cell lines. Heliyon 6:e03273 https://doi.org/10.1016/j.heliyon.2020.e03273
Wilson GL, Lill M (2011) Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 3(6) https://doi.org/10.4155/fmc.11.18
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 31 https://dx.doi.org/10.1002%2Fjcc.21334
Golbraikh A, Wang XS, Zhu H, Tropsha A (2016) Predictive QSAR modeling: methods and applications in drug discovery and chemical risk assessment. In: Leszczynski J (ed) Handbook of Computational Chemistry. Springer, Dordrecht https://doi.org/10.1007/978-94-007-6169-8_37-2
Netzeva TI, Worth A, Aldenberg T et al (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA 33(2):155–173
Aparoy P, Reddy KK, Reddana P (2012) Structure and ligand based drug design strategies in the development of novel 5-LOX inhibitors. Curr Med Chem 19(22):3763–3778 https://dx.doi.org/10.2174%2F092986712801661112
Guedes I, Magalhäes C, Dardenner L (2014) Receptor – ligand molecular docking. Biophys Rev 6(1):75–87 https://dx.doi.org/10.1007%2Fs12551-013-0130-2
Meng X, Zhang H, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7(2):146–157
Arthur DE, Uzairu A, Mamza P, Abechi SE, Shallangwa GA (2018) In silico modelling of quantitative structure-activity relationship of Pgi50 anticancer compounds on k-562 cell line. Cogent Chem 4(1) https://doi.org/10.1080/23312009.2018.1432520
Tropsha A (2010) Best practices for QSAR model development, validation and exploitation. Molecular Informatics 29(6-7):476–488 https://doi.org/10.1002/minf.201000061