QSAR and Molecular Docking Studies of novel thiophene, pyrimidine, coumarin, pyrazole and pyridine derivatives as Potential Anti-Breast Cancer Agent

Year 2020, Volume: 4 Issue: 1, 12 - 23, 15.06.2020

İdris Momohjimoh Ovaku , Abechi Stephehe Eyije Shallangwa Gideon Adamu Uzairu Adamu

https://doi.org/10.33435/tcandtc.614263

Cited By: 2

Abstract

Quantitative Structure Activity Relationship
(QSAR) and molecular Docking studies were carried out on some novel compounds to
generate a good QSAR models that relate the anti-breast cancer activity values
with the molecular structure of the compounds. Genetic Function Algorithm (GFA)
and Multiple Linear Regression Analysis (MLRA) were used to select the
descriptors that were used to build the models. The best model built was found
to have statistical validation values of squared correlation coefficient (R²) = 0.999, adjusted squared
correlation coefficient ( = 0.998, cross validation coefficient  = 0.998 and an external squared correlation coefficient
=
0.879 which was used to confirm the validation of the model. The docking results
showed that ligands 6 and 5 with binding energy (-9.2kcalmol^-1 and
-9.0kcalmol^-1) respectively have the highest binding affinity when
compared to the reference drug doxorubicin with binding energy (-6.8kcalmol^-1).
The stability and robustness of the built model showed that new anti-breast
cancer agents can be design from these derivatives.

Keywords

Keywords: Breast Cancer, QSAR model, Model Validation, Binding Affinity

References

• [1] H. Frankish, 15 Million new cancer cases per year by 2020, says WHO: Lancet. (2003) 361, 1278–1287, DOI: 10.1016/S0140-6736(03)13038-3.
• [2] H.D. Nelson, M.E. Smith, J.C. Griffin, R. Fu, Use of medications to reduce risk for primary breast cancer: a systematic review for the U.S. Preventive Services Task Force. Annals of Internal Medicine 158 (2013) 604-14.
• [3] M.N. Okobia, C.H. Bunker, F.E. Okonofua and U. Osime Knowledge, Attitude and Practice of Nigerian Women towards Breast Cancer: A Cross-Sectional Study. World Journal of Surgical Oncology. 4 (2006) 11–15.
• [4] Rathod, Antifungal and Antibacterial activities of Imidazolyl pyrimidines derivatives and their QSAR Studies under Conventional and Microwave-assisted, Int J PharmTech Res. 3 (2011) 1942–1951.
• [5] Y. Pommier, E. Leo, H. Zhang, and C. Marchand, DNA Topoisomerases and Their Poisoning by Anticancer and Antibacterial Drugs, Chem. Biol. 17 (2010) 421–433.
• [6] Y. Pommier, Y. Sun, S.N. Huang, and J.L. Nitiss, Roles of Eukaryotic Topoisomerases in Transcription, Replication and Genomic Stability, Nat. Rev. Mol. Cell Biol. 17 (2016) 703–721.
• [7] J.E. Deweese, and N. Osheroff, The DNA Cleavage Reaction of Topoisomerase II: Wolf in Sheep’s Clothing. Nucleic Acids Res. 37 (2009) 738–749.
• [8] P. Forterre, and D. Gadelle, Phylogenomics of DNA Topoisomerases: Their Origin and Putative Roles in the Emergence of Modern Organisms, Nucleic Acids Res. 37 (2009) 679–692.
• [9] B. Mukesh, and K. Rakesh, Molecular Docking: A Review. IJRAP, 2 (2011)1746-1751.
• [10] I.A. Guedes, C.S. de Magalhães and L.E. Dardenne, Receptor–Ligand Molecular Docking: Biophysical Reviews. (2014) 6 75-87
• [11] Albratty, M., El-Sharkawy, K. A., and Alam, S., (2017). Synthesis and Antitumor Activity of Some Novel Thiophene, Pyrimidine, Coumarine, Pyrazole and Pyridine Derivatives. Acta Pharm. 67, 15–33, DOI: 10.1515/acph-2017-0004.
• [12] Z. Li, H. Wan, Y. Shi, P. Ouyang, Personal experience with four kinds of chemical structure drawing software: review on ChemDraw, ChemWindow, ISIS/Draw, and ChemSketch, Journal of Chemical Information and Computer Sciences. 44 (2004) 1886–1890.
• [13] C. Lee, W. Yang, R.G. Parr, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density, Physical Review B. 37 (1988) 785.
• [14] A.D. Becke, Becke’s three parameter hybrid method using the LYP correlation functional, J. Chem. Phys. 98 (1993) 5648– 5652.
• [15] S.E. Adeniji, S. Uba, A. Uzairu, In Silico Study For Investigating and Predicting the activities of 1, 2, 4-triazole derivaties as potent anti-tubercular agents, The Journal of Engineering and Exact Sciences. 4 (2018) 246–254.
• [16] P. Singh, Quantitative Structure-Activity Relationship Study of Substituted-[1, 2, 4] Oxadiazoles as S1P1 Agonists, Journal of Current Chemical and Pharmaceutical Sciences. 3 (2013) 334–345.
• [17] G. Melagraki, A. Afantitis, K. Makridima, H. Sarimveis, O. Igglessi-Markopoulou, Prediction of toxicity using a novel RBF neural network training methodology, Journal of Molecular Modeling. 12 (2006) 297–305.
• [18] A. Afantitis, G. Melagraki, H. Sarimveis, P.A. Koutentis, J. Markopoulos, O. IgglessiMarkopoulou, A novel QSAR model for predicting induction of apoptosis by 4-aryl4H-chromenes, Bioorganic & Medicinal Chemistry. 14 (2006) 6686–6694.
• [19] A.K. Chakraborti, B. Gopalakrishnan, M.E. Sobhia, A. Malde, 3D-QSAR studies of indole derivatives as phosphodiesterase IV inhibitors, European Journal of Medicinal Chemistry. 38 (2003) 975–982.
• [20] K.F. Khaled, Modeling corrosion inhibition of iron in acid medium by genetic function approximation method: A QSAR model, Corrosion Science. 53 (2011) 3457–3465.
• [21] A. Tropsha, P. Gramatica, V.K. Gombar, The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Mol. Inform. 22, 69-77, 2003, DOI: 10.1002/qsar.200390007.
• [22] N. Minovski, Š. Župerl, V. Drgan, M. Novič, Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: a case study, Anal. Chim. Acta 759 (2013) 28–42.
• [23] R. Veerasamy, H. Rajak, A. Jain, S. Sivadasan, C.P. Varghese, R.K. Agrawal, Validation of QSAR models-strategies and importance, International Journal of Drug Design & Discovery. 3 (2011) 511–519.
• [24] A. Tropsha, P. Gramatica, V. K. Gombar, The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models, Molecular Informatics. 22 (2003) 69–77.
• [25] http://www.rcsb.org/pdb/home/home.do
• [26] S.E. Adeniji, S. Uba, A. Uzairu, QSAR Modeling and Molecular Docking Analysis of Some Active Compounds against Mycobacterium tuberculosis Receptor (Mtb CYP121), Journal of Pathogens. (2018) 1– 24.