Studies on the IC50 of Metabolically Stable 1-(3,3-diphenylpropyl)- piperidinyl Amides and Ureas as Human CCR5 Receptor Antagonists Based on QSAR

Author(s): Yutao Zhao, Xiaoqian Liu, Jing Ouyang, Yan Wang, Shanyu Xu, Dongdong Tian, Hongzong Si*

Journal Name: Letters in Drug Design & Discovery

Volume 17 , Issue 8 , 2020

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: In this study, modulators of human Chemotactic cytokine receptor 5 (CCR5) were described using a quantitative structure-activity relationship model (QSAR). This model was based on the molecule’s chemical structure.

Methods: All 56 compounds of CCR5 receptor antagonists were randomly separated into two sets, 43 were reserved for training and the other 13 for testing. In the course of this study, molecular models were drawn using ChemDraw software. By means of Hyperchem software as well as optimized with both AM1 (semi-empirical self-consistent-field molecular orbital) and MM+ (molecular mechanics plus force field), molecular models were described through numerous descriptors using CODESSA software.

Results: Linear models were obtained by Heuristic Method (HM) software and nonlinear models were obtained using APS software with optimal descriptor combinations used to build linear QSAR models, involving a group of selected descriptors. As a result, values of the above two different sets were shown to result from 0.82 in testing and 0.86 in training in HM while 0.83 in testing and 0.88 in training in Gene Expression Programming (GEP).

Conclusion: From this method, the activity of molecules could be predicted, and the molecular structure could be changed to alter its IC50, avoiding the testing of large numbers of compounds.

Keywords: CCR5 Receptor antagonists, quantitative structure-activity relationship model, linear model, nonlinear model, descriptor, IC50.

Onuffer, J.J.; Horuk, R. Chemokines, chemokine receptors and small-molecule antagonists: recent developments. Trends Pharmacol. Sci., 2002, 23(10), 459-467.
[] [PMID: 12368070]
Palani, A.; Shapiro, S.; Clader, J.W.; Greenlee, W.J.; Blythin, D.; Cox, K.; Wagner, N.E.; Strizki, J.; Baroudy, B.M.; Dan, N. Biological evaluation and interconversion studies of rotamers of SCH 351125, an orally bioavailable CCR5 antagonist. Bioorg. Med. Chem. Lett., 2003, 13(4), 705-708.
[] [PMID: 12639563]
Winkler, D.A.; Mombelli, E.; Pietroiusti, A.; Tran, L.; Worth, A.; Fadeel, B.; McCall, M.J. Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. Toxicology, 2013, 313(1), 15-23.
[] [PMID: 23165187]
Guven, A.; Aytek, A. New approach for stage-discharge relationship: Gene-expression programming. J. Hydrol. Eng., 2009, 14, 812-820.
Eriksson, L.; Andersson, P.L.; Johansson, E.; Tysklind, M. Megavariate analysis of environmental QSAR data. Part I--a basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD). Mol. Divers., 2006, 10(2), 169-186.
[] [PMID: 16770514]
Duan, L.; Tang, C.; Zhang, T.; Wei, D.; Zhang, H. Distance guided classification with gene expression programming. Adv. Data Min. Appl., 2006, 4093, 239-246.
Song, F.; Zhang, A.; Liang, H.; Cui, L.; Li, W.; Si, H.; Duan, Y.; Zhai, H. QSAR study for carcinogenic potency of aromatic amines based on GEP and MLPs. Int. J. Environ. Res. Public Health, 2016, 13(11), 1141.
[] [PMID: 27854309]
Reichelt, A.; Bailis, J.M.; Bartberger, M.D.; Yao, G.; Shu, H.; Kaller, M.R.; Allen, J.G.; Weidner, M.F.; Keegan, K.S.; Dao, J.H. Synthesis and structure-activity relationship of trisubstituted thiazoles as Cdc7 kinase inhibitors. Eur. J. Med. Chem., 2014, 80, 364-382.
[] [PMID: 24793884]
de Assis, T.M.; Gajo, G.C.; de Assis, L.C.; Garcia, L.S.; Silva, D.R.; Ramalho, T.C.; da Cunha, E.F. qsar models guided by molecular dynamics applied to human glucokinase activators. Chem. Biol. Drug Des., 2016, 87(3), 455-466.
[] [PMID: 26547388]
Ge, C.Z.; Song, F.C.; Gao, H.; Zhai, H.L.; Si, H.Z. 3D-QSAR studies on quinazoline derivatives as EGFR-T790M inhibitors by comparative molecular field analysis (CoMFA). Cancer Cell Res., 2015, 2, 201-209.
4.0, Hypercube 1994.
Stewart, O.P.P. MOPAC 6.0, QCPE, No. 455, Quantum Chemistry Program Exchange; Indiana University: Bloomington, IN, 1989.
Katritzky, A.R.; Lobanov, V.S.; Karelson, M.; Murugan, R.; Grendze, M.P.; Toomey, J.E. Comprehensive descriptors for structural and statistical analysis. 1. Correlations between structure and physical properties of substituted pyridines. Rev. Roum. Chim., 1996, 41, 851-868.
Wright, J.S.; Caipenter, D.J.; Mckay, D.J.; Ingold, K. Theoretical calculation of substituent effects on the O-H bond strength of phenolic antioxidants related to vitamin E. J. Am. Chem. Soc., 1997, 119, 4245-4252.
Janardhan, S.; Srivani, P.; Sastry, G.N. 2D and 3D Quantitative structure-activity relationship studies on a series of bis-pyridinium compounds as choline kinase inhibitors. QSAR Comb. Sci., 2006, 25, 860-872.
Song, J.; Wang, Z.D.; Han, Z.J.; Chen, J.Z.; Jiang, Z.K.; Zheng, S.B. A OASR study of the repellency of some terpenoids to the bed bug Cimex lectularius L. Linchan Huaxue Yu Gongye, 2012, 32, 1-8.
Kunal, R.; Kar, S.; Ambure, P. On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst., 2015, 145
Ćirić Zdravković, S.; Pavlović, M.; Apostlović, S.; Koraćević, G.; Šalinger Martinović, S.; Stanojević, D.; Sokolović, D.; Veselinović, A.M. Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition-In silico approach. Comput. Biol. Chem., 2019, 79, 55-62.
[] [PMID: 30716601]
Roy, P.P.; Roy, K. On some aspects of variable selection for partial least squares regression models. QSAR Comb. Sci., 2008, 27, 302.
Roy P.P., ; Paul, S.; Mitra, I.; Roy, K. On two novel parameters for validation of predictive QSAR models. Molecules, 2009, 14, 1660-1701.
[] [PMID: 19471190]
Mitra, I.; Roy, P.P.; Kar, S.; Ojha, P.; Roy, K. On further application of r2m as a metric for validation of QSAR. J. Chemometrics,., 2010, 24, 22-33.
Ojha, P.K.; Mitra, I.; Das, R.N.; Roy, K. Chemom. Intell. Lab. Syst., 2011, 107, 194.
Roy, K.; Mitra, I.; Kar, S.; Ojha, P.K.; Das, R.N.; Kabir, H. Comparative studies on some metrics for external validation of QSPR models. J. Chem. Inf. Model., 2012, 52(2), 396-408.
[] [PMID: 22201416]
Roy, K.; Mitra, I.; Ojha, P.K.; Kar, S.; Das, R.N.; Kabir, H. Introduction of rm2(rank) metric incorporating rank-order predictions as an additional tool for validation of QSAR/QSPR models. Chemom. Intell. Lab. Syst., 2012, 118, 200.
Roy, K.; Chakraborty, P.; Mitra, I.; Ojha, P.K.; Kar, S.; Das, R.N. Some case studies on application of “r(m)2” metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. J. Comput. Chem., 2013, 34(12), 1071-1082.
[] [PMID: 23299630]

open access plus

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [1036 - 1046]
Pages: 11
DOI: 10.2174/1570180817666200320105725

Article Metrics

PDF: 24