Investigation of Drug Interaction Potentials and Binding Modes on Direct Renin Inhibitors: A Computational Modeling Studies

Author(s): Lakshmanan Loganathan, Karthikeyan Muthusamy*.

Journal Name: Letters in Drug Design & Discovery

Volume 16 , Issue 8 , 2019

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Graphical Abstract:


Abstract:

Background: Hypertension is one of the key risk factors for cardiovascular disease, it is regulated through Renin Angiotensin Aldosterone System (RAAS) cascade. Renin catalyzes the initial rate-limiting step in RAAS system, that influences the synthesis of angiotensin I from precursor angiotensin. Renin inhibition could be a potential step for the blood pressure lowering mechanism as well as for organ protection.

Methods: In order to understand the structure-activity association of direct renin inhibitors (DRIs), we have carried out three-dimensional quantitative structure activity relationship (3D-QSAR), molecular docking studies and Density Functional Theory (DFT) analysis to identify the attractive compounds. Five-point pharmacophore model of one acceptor, three hydrophobic groups and one aromatic ring was chosen for the dataset of 40 compounds.

Results: The generated 3D-QSAR model shows that the alignment has a good correlation coefficient for the training set compounds, which comprise the value of R2 = 0.96, SD = 0.1, and F = 131.3. The test compounds had Q2 = 0.91, RMSE = 0.25, and Pearson-R = 0.97, which describes the predicted model was reliable.

Discussion: External validations were carried out to validate the predicted QSAR model. Further, the significant compounds were studied using different in silico approaches in order to explore the difference in the atomic configuration and binding mechanism of the identified compounds.

Conclusion: The molecular dynamics simulation of the complex was analyzed and confirmed the stability of the compounds in the protein. The outcome of the result could be useful to improve the safety and efficacy of DRIs that can be projected to clinical trials.

Keywords: Renin inhibitors, hypertension, pharmacophore, renin, 3D-QSAR, DFT, molecular dynamics.

[1]
Paulis, L.; Rajkovicova, R.; Simko, F. New developments in the pharmacological treatment of hypertension: Dead-end or a glimmer at the horizon? Curr. Hypertens. Rep., 2015, 17(6), 557.
[2]
Noncommunicable diseases progress monitor, 2017. Geneva: World Health Organization. 2017. Licence: CC BY-NC-SA 3.0 IGO
[3]
Saraceno, B. World Health Day. Acta Psychiatr. Scand., 2001, 103(2), 83.
[4]
Campbell, D. Renin inhibitors-mechanisms of action. Aust. Prescr., 2009, 32(5), 132-135.
[5]
Holsworth, D.D.; Cai, C.; Cheng, X-M.; Cody, W.L.; Downing, D.M.; Erasga, N.; Lee, C.; Powell, N.A.; Edmunds, J.J.; Stier, M.; Jalaie, M.; Zhang, E.; McConnell, P.; Ryan, M.J.; Bryant, J.; Li, T.; Kasani, A.; Hall, E.; Subedi, R.; Rahim, M.; Maiti, S. Ketopiperazine-based renin inhibitors: Optimization of the “C” ring. Bioorg. Med. Chem. Lett., 2006, 16(9), 2500-2504.
[6]
Calixto, A.R.; Bras, N.F.; Fernandes, P.A.; Ramos, M.J. Reaction mechanism of human renin studied by quantum mechanics/molecular mechanics(QM/MM) Calculations. ACS Catal., 2014, 4(11), 3869-3876.
[7]
Pool, J.L. Direct renin inhibition: Focus on aliskiren. J. Manag. Care Pharm., 2007, 13(8)(Suppl. B), 21-33.
[8]
Tani, S.; Kushiro, T.; Takahashi, A.; Kawamata, H.; Ohkubo, K.; Nagao, K.; Hirayama, A. Antihypertensive efficacy of the direct renin inhibitor aliskiren as add-on therapy in patients with poorly controlled hypertension. Intern. Med., 2016, 55(5), 427-435.
[9]
Desjarlais, M.; Dussault, S.; Dhahri, W.; Mathieu, R.; Rivard, A. direct renin inhibition with aliskiren improves ischemia-induced neovascularization: Blood pressure-independent effect. Atherosclerosis, 2015, 242(2), 450-460.
[10]
Buczko, W.; Hermanowicz, J.M. Pharmacokinetics and pharmacodynamics of aliskiren, an oral direct renin inhibitor. Pharmacol. Rep., 2008, 60(5), 623-631.
[11]
McMurray, J.J.V.; Krum, H.; Abraham, W.T.; Dickstein, K.; Køber, L.V.; Desai, A.S.; Solomon, S.D.; Greenlaw, N.; Ali, M.A.; Chiang, Y.; Shao, Q.; Tarnesby, G.; Massie, B.M. Aliskiren, enalapril, or aliskiren and enalapril in heart failure. N. Engl. J. Med., 2016, 374(16), 1521-1532.
[12]
Prescrire, R. Towards better patient care: Drugs to avoid in 2015. Prescrire Int., 2014, 23(150), 161-165.
[13]
Webb, R.L.; Schiering, N.; Sedrani, R.; Maibaum, J. Direct renin inhibitors as a new therapy for hypertension. J. Med. Chem., 2010, 53(21), 7490-7520.
[14]
Nakamura, Y.; Sugita, C.; Meguro, M.; Miyazaki, S.; Tamaki, K.; Takahashi, M.; Nagai, Y.; Nagayama, T.; Kato, M.; Suemune, H.; Nishi, T. Design and optimization of novel(2S,4S,5S)-5-amino-6-(2,2-dimethyl-5-oxo-4-phenylpiperazin-1-yl)-4-hydroxy-2-isopropylhexanamides as renin inhibitors. Bioorg. Med. Chem. Lett., 2012, 22(14), 4561-4566.
[15]
Mori, Y.; Ogawa, Y.; Mochizuki, A.; Nakamura, Y.; Sugita, C.; Miyazaki, S.; Tamaki, K.; Matsui, Y.; Takahashi, M.; Nagayama, T.; Nagai, Y.; Inoue, S.I.; Nishi, T. Design and discovery of new(3s,5r)-5-[4-(2-chlorophenyl)-2,2-dimethyl-5-oxopiperazin-1-yl]piperidine-3-carboxamides as potent renin inhibitors. Bioorg. Med. Chem. Lett., 2012, 22(24), 7677-7682.
[16]
Li, Y.; Wang, Y.; Zhang, F. Pharmacophore modeling and 3d-qsar analysis of phosphoinositide 3-kinase p110alpha inhibitors. J. Mol. Model., 2010, 16(9), 1449-1460.
[17]
Dixon, S.L.; Smondyrev, A.M.; Knoll, E.H.; Rao, S.N.; Shaw, D.E.; Friesner, R.A. PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput. Aided Mol. Des., 2006, 20(10-11), 647-671.
[18]
Selvaraman, N.; Selvam, S.K.; Muthusamy, K. The binding mode prediction and similar ligand potency in the active site of vitamin D receptor with QM/MM interaction, MESP, and MD simulation. Chem. Biol. Drug Des., 2016, 88(2), 272-280.
[19]
Bhattacherjee, D.; Bhabak, K.P. Atom based 3D-QSAR studies on 2,4-dioxopyrimidine-1-carboxamide analogs: Validation of experimental inhibitory potencies towards acid ceramidase. Eur. J. Pharm. Sci., 2016, 83, 8-18.
[20]
Nagamani, S.; Muthusamy, K.; Kirubakaran, P.; Singh, K.D.; Krishnasamy, G. Theoretical studies on benzimidazole derivatives as E. coli biotin carboxylase inhibitors. Med. Chem. Res., 2012, 21(9), 2169-2180.
[21]
Roy, K.; Das, R.N.; Ambure, P.; Aher, R.B. Be Aware of Error Measures. Further Studies on Validation of Predictive QSAR Models; Elsevier B.V., 2016, Vol. 152, .
[22]
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49(21), 6177-6196.
[23]
van der Spoel, D.; Drunen, R.V.; Berendsen, H.J.C. GROMACS : Groningen Machine for Chemical Simulations User Manual Version 3.3.3; , 1994.
[24]
Schüttelkopf, A.W.; Van Aalten, D.M.F. PRODRG: A tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. Sect. D Biol. Crystallogr, 2004, 60(8), 1355-1363.
[25]
Gharaghani, S.; Khayamian, T.; Keshavarz, F. Docking, molecular dynamics simulation studies, and structurebased QSAR model on cytochrome P450 2A6 inhibitors. Struct. Chem., 2012, 23(2), 341-350.
[26]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov., 2015, 10(5), 449-461.
[27]
Misini-Ignjatovic, M.; Caldararu, O.; Dong, G.; Munoz-Gutierrez, C.; Adasme-Carreno, F.; Ryde, U. Binding-affinity predictions of HSP90 in the D3R grand challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations. J. Comput. Aided Mol. Des., 2016, 30(9), 707-730.
[28]
Wan, J.; Zhang, L.; Yang, G.; Zhan, C.G. Quantitative structure-activity relationship for cyclic imide derivatives of protoporphyrinogen oxidase inhibitors: A study of quantum chemical descriptors from density functional theory. J. Chem. Inf. Comput. Sci., 2004, 44(6), 2099-2105.
[29]
Kirubakaran, P.; Karthikeyan, M. Pharmacophore modeling, 3D-QSAR and DFT studies of IWR small-molecule inhibitors of wnt response. J. Recept. Signal Transduct. Res., 2013, 33(5), 276-285.
[30]
Gupta, M.K.; Misra, K. Atom-based 3D-QSAR, molecular docking and molecular dynamics simulation assessment of inhibitors for thyroid hormone receptor α and β. J. Mol. Model., 2014, 20(6), 2286.
[31]
Wood, J.M.; Maibaum, J.; Rahuel, J.; Grütter, M.G.; Cohen, N.C.; Rasetti, V.; Rüger, H.; Göschke, R.; Stutz, S.; Fuhrer, W.; Schilling, W.; Rigollier, P.; Yamaguchi, Y.; Cumin, F.; Baum, H.P.; Schnell, C.R.; Herold, P.; Mah, R.; Jensen, C.; O’Brien, E.; Stanton, A.; Bedigian, M.P. Structure-based design of aliskiren, a novel orally effective renin inhibitor. Biochem. Biophys. Res. Commun., 2003, 308(4), 698-705.
[32]
Politi, A.; Durdagi, S.; Moutevelis-Minakakis, P.; Kokotos, G.; Mavromoustakos, T. Development of accurate binding affinity predictions of novel renin inhibitors through molecular docking studies. J. Mol. Graph. Model., 2010, 29(3), 425-435.
[33]
Lorthiois, E.; Breitenstein, W.; Cumin, F.; Ehrhardt, C.; Francotte, E.; Jacoby, E.; Ostermann, N.; Sellner, H.; Kosaka, T.; Webb, R.L.; Rigel, D.F.; Hassiepen, U.; Richert, P.; Wagner, T.; Maibaum, J. The discovery of novel potent trans-3,4-disubstituted pyrrolidine inhibitors of the human aspartic protease renin from in silico three-dimensional(3D) pharmacophore searches. J. Med. Chem., 2013, 56(6), 2207-2217.
[34]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development setting. Adv. Drug Deliv. Rev., 2012, 64, 4-17.


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Article Details

VOLUME: 16
ISSUE: 8
Year: 2019
Page: [919 - 938]
Pages: 20
DOI: 10.2174/1570180815666180827113622
Price: $65

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