QSAR/ANN approaches and molecular docking applied to calcium channel blockers

Year 2024, Volume: 8 Issue: 4, 1 - 16, 02.12.2024

Siham Aggoun , Salah Belaıdı , Lazhar Bouchlaleg , Hassan Nour , Oussama Abchır , Samir Chtita , Muneerah Almogren , Majdi Hochlaf

https://doi.org/10.33435/tcandtc.1319350

Abstract

Artificial neural networks (ANN) are very useful for predicting biological activities in QSAR studies. ANNs allow the study of complex and nonlinear SAR. We use ANN and MLR methods to generate QSAR models for Calcium Channel Blockers activity of a series of 1,4-dihydropyridines. Molecular descriptors were calculated by using DFT method at the B3LYP/6-31G+ (d, p) level. Statistical analyzes show that the predicted values of the activities are in excellent agreement with the experimental results. Molecular docking studies have been performed, in order to re-estimate the activity of molecules as CCBs by analyzing their binding energies and mutual interaction types.

Keywords

dihydropyridine, Calcium Channel Blockers, QSAR, DFT, ANN, MLR

References

• [1] A. Hantzsch, Condensation products made of aldehydammoniak and keton-like connections, Chem. Ber. 14, (1881) 1637-1638.
• [2] E. EisnerE, J. Kuthan, Chemistry of dihydropyridines, Chem. Rev. 72, (1972) 1-42.
• [3] D.J. Triggle, 1,4-Dihydropyridines as calcium channel ligands and privileged structures ,Cell. Mol. Neurobiol 23, (2003) 293-303.
• [4] N. Edraki, A.R. Mehdipour, M. Khoshneviszadeh, R. Miri, Dihydropyridines: evaluation of their current and future pharmacological applications, Drug Discov. Today 14 (2009) 1058-66.
• [5] F. Bossert, W. Vater, 1,4-Dihydropyridines-a basis for developing new drugs, Med. Res. Rev. 9 (1989) 291-324.
• [6] F. Bossert, W. Vater, Dihydropyridine, eine neue Gruppe stark wirksamer Coronartherapeutika [Dihydropyridines, a new group of strongly effective coronary therapeutic agents], Natur wissenschaften 58 (1971) 578.
• [7] M. Epstein, H.R. Black, Arterial Calcification and Calcium Antagonists,Hypertension 37 ( 2001) 1414–1415.
• [8] V. Klusa, A typical 1,4-dihydropyridine derivatives, an approach to neuroprotection and memory enhancement, Pharmacol. Res. 113 (2016) 754-759.
• [9] V.K. Sharma, S.K. Singh, Synthesis, utility and medicinal importance of 1,2- & 1,4-dihydropyridines, RSC Adv. 7 (2017) 2682–2732.
• [10] C.A. Frank, J.M. Forst, R.J. Harris ,S.T.Kau, J.H. Li, C.J. Ohmmachb, R.W. Smith, D.A.Trainor, S.Trivedi, Dihydropyridine KATP potassium channel openers, Bioorg. Med. Chem. Lett. 3 (1993) 2725–2726.
• [11] R.H.Bocker, F.P. Guengerich, Oxidation of 4-aryl- and 4-alkyl-substituted 2,6-dimethyl-3,5-bis(alkoxycarbonyl)-1,4-dihydropyridines by human liver microsomes and immunochemical evidence for the involvement of a form of cytochrome P-450,J. Med. Chem. 29 (1986) 1596-1603.
• [12] P. Ioan, E. Carosati, M. Micucci, G. Cruciani, F. Broccatelli, B.S. Zhorov, A. Chiarini, R. Budriesi, 1,4-Dihydropyridine Scaffold in Medicinal Chemistry, The Story so Far And Perspectives (Part 1): Action in Ion Channels and GPCRs, Current Medicinal Chemistry 18 (2011)4901-4922.
• [13] Q. Huang, Y. Li, C. Sheng, Y. Dou, M. Zheng, Z. Zhu, J. Wang, Blood pressure lowering efficacy of amlodipine and nifedipine-Gits in ambulatory hypertension, J. Hypertens, 33 (2015) 94.
• [14] M.F.Gordeev, D.V. Patel, E.M. Gordon, Approaches to the combinatorial synthesis of heterocycles: a solid-phase synthesis of 1,4-dihydropyridines, J.Org.Chem. 61(1996) 924-928.
• [15] B. Hemmateenejad, M.A. Safarpour, R. Miri, F. Taghavi, Application of ab initio theory to QSAR study of 1,4-dihydropyridine-based calcium channel blockers using GA-MLR and PC-GA-ANN procedures, J. Comput. Chem 25(2004) 1495–1503.
• [16] R. Mannhold, B. Jablonka, W. Voigdt, K. Schoenafinger, K. Schravan, Calcium- and calmodulin-antagonism of elnadipine derivatives: comparative SAR, Eur. J. Med. Chem. 27(1992) 229-235.
• [17] L. Yet, 1,4-dihydropyridines. In: Privileged Structures in Drug Discovery. Hoboken, NJ, USA: John Wiley & Sons, Inc 5(2018) 9–82.
• [18] M. Rucins, A. Plotniece, E. Bernotiene, W-B. Tsai, A. Sobolev, Recent Approaches to Chiral 1,4-Dihydropyridines and their Fused Analogues, Catalysts 10 (2020) 1019.
• [19] R. Hanachi, A. Ben Said, H. Allal, S. Rahali, M.A.M. Alkhalifah, F. Alresheedi, B. Tangour, M. Hochlaf, Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists, New J. Chem. 45 (2021) 17796-17807.
• [20] M.A .Safarpour, B. Hemmateenejad, R. Miri, M. Jamali, Quantum Chemical-QSAR Study of Some Newly Synthesized 1,4-Dihydropyridine Calcium Channel Blockers. QSAR & Combinatorial Science 22 (2003) 997–1005.
• [21] X. Yao, H. Liu, R. Zhang, M. Liu, Z. Hu, A. Panaye, J.P. Doucet, B. Fan, QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines, Mol. Pharm. 2 (2005) 348-56.
• [22] F. Hadizadeh, S. Vahdani, M. Jafarpour, Quantitative Structure-Activity Relationship Studies of 4-Imidazolyl- 1,4-dihydropyridines as Calcium Channel Blockers, Iran. J. Basic Med. Sci. 16 (2013) 910-916.
• [23] E.G. Da Mota, D.G. Silva, M.C. Guimarães, E.F.F. da Cunha, M.P. Freitas, Computer-assisted design of novel 1,4-dihydropyridine calcium channel blockers, Mol. Simul. 40 (2014) 959-965.
• [24] C. Jardínez, A. Vela, J. Cruz-Borbolla, R.J. Alvarez-Mendez, J. G. Alvarado-Rodríguez, Reduced density gradient as a novel approach for estimating QSAR descriptors, and its application to 1,4-dihydropyridine derivatives with potential antihypertensive effects, J. Mol. Model. 22(2016) 296.
• [25] T.F. El-Moselhy, P.A. Sidhom, E.A. Esmat, N.A. El-Mahdy,Synthesis, Docking Simulation, Biological Evaluations and 3D-QSAR Study of 1,4-Dihydropyridines as Calcium Channel Blockers, Chem. Pharm. Bull. (Tokyo) 65(2017) 893-903.
• [26] L. Navidpour, R. Miri, A. Shafiee, Synthesis and calcium channel antagonist activity of new 1,4-dihydropyridine derivatives containing lipophilic 4-imidazolyl substituents, Arzneim. Forsch. Drug Res. 54 (2004) 499-504.
• [27] HyperChem (Molecular Modeling System) Hypercube (2008) Gainesville FL 32601, USA
• [28] G. Schneider, K.H. Baringhaus, H. Kubinyi, Molecular Design: Concepts and Applications, John Wiley & Sons,2008.
• [29] C.A. Lipinski, F. Lombardo, B. W. Dominy, P. Feeney, J. Adv. Drug. Deliv. Rev. 23 (1997) 3-25.
• [30] D.F. Veber,S.R. Johnson, H.Y. Cheng, B.R, Smith, K.W. Ward, K.D. Kopple, Molecular Properties That Influence the Oral Bioavailability of Drug Candidates ,J.Med.Chem. 45 (2002) 2615-2623.
• [31] A.K. Ghose, V.N. Viswanadhan, J.J. Wendoloski, J. Comb. Chem 1 (1999) 55.
• [32] M. Ouassaf, S. Belaidi, S. Khamouli, H. Belaidi, S. Chtita , Combined 3D-QSAR and molecular docking analysis of thienopyrimidine derivatives as staphylococcus aureus inhibitors, Acta. Chim. Slov. 68 (2021) 289–303.
• [33] M.Ouassaf, S. Belaidi, K. Lotfy, K.I. Daou, H. Belaidi, Molecular docking studies and ADMET properties of new 1.2. 3 triazole derivatives for anti-breast cancer activity, J. Bionanosci. 12 (2018) 1-11.
• [34] ADT / AutoDockTools — AutoDock. http://autodock.scripps.edu/resources/adt (Accessed : 28.05. 2022).
• [35] M.D.Hanwell, D.E. Curtis, D.C. Lonie, T. Vandermeerschd, E. Zurek, G.R.Hutchison, Avogadro: an advanced semantic chemical editor, visualization, and analysis platform,” J. Cheminform 17 (2012) 4.
• [36] BIOVIA Discovery Studio - BIOVIA - Dassault Systèmes®.
• [37] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.R. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery, J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J.M. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, O. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, (2009) Gaussian 09, revision D.01. Gaussian Inc, Wallingford
• [38] M.Alloui, S. Belaidi, H. Othmani, N.E. Jaidane, M.Hochlaf, Imidazole derivatives as angiotensin II AT1 receptor blockers : Benchmarks, drug-like calculations and quantitative structure-activity relationships modeling,Chem. Phys. Lett. 696 (2018) 70–78.
• [39] S.Boudergua, M. Alloui, S. Belaidi, M. Mogren, U.A Al Mogren, Abd Ellatif Ibrahim, M. Hochlaf, QSAR Modeling and Drug-Likeness Screening for Antioxidant Activity of Benzofuran Derivatives, J. Mol. Struct. 1189 (2019) 307-314.
• [40] M. Ghamri, D. Harkati ,S. Belaidi, S. Boudergua, R. Linguerri, G. Chambaud, M. Hochlaf, Carbazole derivatives containing chalcone analogues targeting topoisomerase II inhibition: first principles characterization and QSAR modeling. Spectrochim. Acta A 242 (2020) 118724.
• [41] I. Almi, S. Belaidi, A. Zerroug, M. Alloui, R. Ben Said, R. Linguerri, M. Hochlaf, QSAR investigations and structure-based virtual screening on a series of nitrobenzoxadiazole derivatives targeting human glutathione-S-transferases. J. Mol. Struct. 1211 (2020) 128015.
• [42] M. Manachou, Z. Gouid, Z. Almi, S. Belaidi, S. Boughdiri, M. Hochlaf, Pyrazolo[1,5- a][1,3,5] triazin-2thioxo-4-ones derivatives as thymidine phosphorylase inhibitors: Structure, drug-like calculations and quantitative structure-activity relationships (QSAR) modeling, J. Mol. Struct. 1199 (2020) 127027.
• [43] S.Chtita, M. Ghamali, R. Hmamouchi, B. Elidrissi, B. Bourass, M. Larif,M. Bouachrine, T. Lakhlifi, Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using Quantitative Structure Activity Relationship Analysis, Advances in Physical Chemistry 5137289(2016) 16.
• [44] D. Eros, I. Kövesdi, L.Orfi, K. Takács-Novák, GY. Acsády, Gy. Kéri, Reliability of logP predictions based on calculated molecular descriptors: a critical review,Curr. Med. Chem. 9 (2002) 1819-29.
• [45] A. Ambati, C.S. Canakis, J.W. Miller, E.S. Gragoudas, E. Edwards, D.J. Weissgold, I. Kim, F.C. Delori, A.P. Adamis, Diffusion of High Molecular Weight Compounds through Sclera, Investig. Ophthalmol. Vis. Sci. 41 (2000) 1181-1185.
• [46] C.A. Lipinski,F. Lombardo, W. Dominy, P. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, J. Adv, Drug Deliv. Rev. 46 (2001) 3-26.
• [47] A. Zerroug, S. Belaidi, I. BenBrahim, L. Sinha, S. Chtita, Virtual screening in drug-likeness and structure/activity relationship of pyridazine derivatives as Anti-Alzheimer drugs, J. King Saud Univ. Sci. 31 (2019) 595-601.
• [48] G. Schaftenaar, J. deVlieg, J. Comput. Aided Mol.Des 26 (2012) 311–318.
• [49] K. Dermeche, N. Tchouar, S. Belaidi, T. Salah, Qualitative Structure-Activity Relationships and 2D-QSAR Modeling of TNF-α Inhibition by Thalidomide Derivatives, J. Bionanosci. 9 (2015) 395-400.
• [50] A. Kerassa, S. Belaidi, D. Harkati, T. Lanez, O. Prasad, L. Sinha, Investigations on Molecular Structure, Electronic Properties, NLO Properties and Comparison of Drug-Likeness of Triazolothiadiazole Derivatives by Quantum Methods and QSAR Analysis, Rev. Theor. Sci. 4 (2016) 85-96.
• [51] A. Mauri,V. Consonni, R. Todeschini, Handbook of Computational Chemistry, Springer, Cham, (2017) 2065-2093.
• [52] XLSTAT software, XLSTAT Company. https://www.xlstat.com.
• [53] MATLAB 7.9.0 (R2009b) and Statistics Toolbox Release, the Math Works‖, Inc., Natick, Massachusetts, United States, 2011.
• [54] S. Chtita, M.Ghamali, R. Hmamouchi, M. Larif, M. Bouachrine, T. Lakhlifi , Quantitative structure–activity relationship studies of anticancer activity for Isatin (1H-indole-2,3-dione) derivatives based on density functional, IJQSPR 2(2017) 90-115.
• [55] L.Eriksson, J. Jaworska, A.P. Worth, M.T. Cronin, R.M. McDowell, P.Gramatica, Methods for reliability and uncertainty assessment and for applicability evaluations of classifcation-and regression-based QSARs, Environ. Health Perspect. 111 (2003) 1361–1375.
• [56] T.I. Netzeva, A.P. Worth, T. Aldenberg, R. Benigni, M.T. Cronin, P.Gramatica, J.S. Jaworska, S. Kahn, G. Klopman, C.A. Marchant, Current status of methods for defning the applicability domain of quantitative structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern. Lab. Anim. 33 (2005) 155–173.
• [57] J. Jaworska, N. Nikolova-Jeliazkova, T. Aldenberg, QSAR applicability domain estimation by projection of the training set in descriptor space, Altern. Lab. Anim. 33(2005) 445–459.
• [58] E. Zerroug, S. Belaidi, S. Chtita, Artificial neural network‐based quantitative structure–activity relationships model and molecular docking for virtual screening of novel potent acetylcholinesterase inhibitors, J. Chin. Chem. Soc. 68 (2021) 1379-1399
• [59] J. Luo, J. Hu, L. Fu, C. Liu, X. Jin, Use of Artificial Neural Network for a QSAR Study on Neurotrophic Activities of N-p-Tolyl/phenylsulfonyl L-Amino Acid Thiolester Derivatives, Procedia Eng. 15(2011) 5158-5163.
• [60] L.Terfloth, J.Gasteiger, Neural networks and genetic algorithms in drug design, DDT 6(2001) 102-108.
• [61] F. Cheng,V.Sutariya, Applications of Artificial Neural Network Modeling in Drug Discovery, Clin. Exp. Pharmacol. 2(2012) 113.
• [62] B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, NY United States, USA, 1996.