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Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Research Article

Computational Modeling on Binding Interactions of Cyclodextrin s with the Human Multidrug Resistance P-glycoprotein Toward Efficient Drug-delivery System Applications

Author(s): Michael González-Durruthy*, Riccardo Concu*, Laura F. Osmari Vendrame, Mirkos Ortiz Martins, Ivana Zanella, Juan Manuel Ruso and Maria Natália Dias Soeiro Cordeiro*

Volume 23, Issue 1, 2023

Published on: 13 May, 2022

Page: [62 - 75] Pages: 14

DOI: 10.2174/1568026622666220303115102

Price: $65

Abstract

Background: Herein, molecular docking approaches and DFT ab initio simulations were combined for the first time, to study the key interactions of cyclodextrins (CDs: α-CD, β-CD, and γ-CD) family with potential pharmacological relevance and the multidrug resistance P-gp protein toward efficient drug-delivery applications.

The treatment of neurological disorders and cancer therapy where the multiple drug-resistance phenomenon mediated by the P-gp protein constitutes the fundamental cause of unsuccessful therapies.

Objectives: To understand more about the CD docking mechanism and the P-gp.

Methods: In order to achieve the main goal, the computational docking process was used. The observed docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions,and also hybrid electrostatic/side-chain interactions of the CD-ligands' OHmotifs with acceptor and donor characteristics, which might theoretically cause local perturbations in the TMD/P-gp inter-residues network, influencing ligand extrusion through the blood-brain barrier. P-gp residues were conformationally favored. Despite the structural differences, all the cyclodextrins exhibit very close Gibbs free binding energy values (or affinity) by the P-gp binding site (transmembrane domains - TMDs).

Result: The obtained theoretical docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions, and also hybrid electrostatic/side-chain interactions of the OH-motifs of the CD-ligands with acceptor and donor properties which theoretically could induce allosteric local-perturbations in the TMDs-inter-residues network of P-gp modulating to the CD-ligand extrusion from the blood-brain-barrier (or cancer cells).

Conclusion: Finally, these theoretical results open new horizons for evaluating new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery applications and precision nanomedicine.

Keywords: Cyclodextrins, P-glycoprotein, ab initio-DFT, Molecular docking, Nanomedicine, Computational modeling, Binding interactions, Drug selivery system, Multidrug resistance.

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[1]
DeGorter, M.K.; Xia, C.Q.; Yang, J.J.; Kim, R.B. Drug transporters in drug efficacy and toxicity. Annu. Rev. Pharmacol. Toxicol., 2012, 52(1), 249-273.
[http://dx.doi.org/10.1146/annurev-pharmtox-010611-134529] [PMID: 21942630]
[2]
He, Q.; Liu, J.; Liang, J.; Liu, X.; Li, W.; Liu, Z.; Ding, Z.; Tuo, D. Towards improvements for penetrating the blood-brain barrier-recent progress from a material and pharmaceutical perspective. Cells, 2018, 7(4), 24.
[http://dx.doi.org/10.3390/cells7040024] [PMID: 29570659]
[3]
Holohan, C.; Van Schaeybroeck, S.; Longley, D.B.; Johnston, P.G. Cancer drug resistance: An evolving paradigm. Nat. Rev. Cancer, 2013, 13(10), 714-726.
[http://dx.doi.org/10.1038/nrc3599] [PMID: 24060863]
[4]
De Lange, E.C.M.; Vd Berg, D.J.; Bellanti, F.; Voskuyl, R.A.; Syvänen, S. P-glycoprotein protein expression versus functionality at the blood-brain barrier using immunohistochemistry, microdialysis and mathematical modeling. Eur. J. Pharm. Sci., 2018, 124, 61-70.
[http://dx.doi.org/10.1016/j.ejps.2018.08.022] [PMID: 30144528]
[5]
Wu, J.; Lin, N.; Li, F.; Zhang, G.; He, S.; Zhu, Y.; Ou, R.; Li, N.; Liu, S.; Feng, L.; Liu, L.; Liu, Z.; Lu, L. Induction of P-glycoprotein expression and activity by Aconitum alkaloids: Implication for clinical drug-drug interactions. Sci. Rep., 2016, 6(1), 25343.
[http://dx.doi.org/10.1038/srep25343] [PMID: 27139035]
[6]
Kim, N.; Shin, J.; No, K.T. In silico study on the interaction between pglycoprotein and its inhibitors at the drug binding pocket. Bull. Korean Chem. Soc., 2014, 35(8), 2317-2325.
[http://dx.doi.org/10.5012/bkcs.2014.35.8.2317]
[7]
Wongrattanakamon, P.; Lee, V.S.; Nimmanpipug, P.; Sirithunyalug, B.; Chansakaow, S.; Jiranusornkul, S. Insight into the molecular mechanism of P-glycoprotein mediated drug toxicity induced by bioflavonoids: An integrated computational approach. Toxicol. Mech. Methods, 2017, 27(4), 253-271.
[http://dx.doi.org/10.1080/15376516.2016.1273428] [PMID: 27996361]
[8]
Zhou, S.F. Structure, function and regulation of P-glycoprotein and its clinical relevance in drug disposition. Xenobiotica, 2008, 38(7-8), 802-832.
[http://dx.doi.org/10.1080/00498250701867889] [PMID: 18668431]
[9]
Salim, S. Oxidative stress and the central nervous system. J. Pharmacol. Exp. Ther., 2017, 360, 201-205.
[http://dx.doi.org/10.1124/jpet.116.237503]
[10]
Montesinos, R.N.; Moulari, B.; Gromand, J.; Beduneau, A.; Lamprecht, A.; Pellequer, Y. Coadministration of P-glycoprotein modulators on loperamide pharmacokinetics and brain distribution. Drug Metab. Dispos., 2014, 42(4), 700-706.
[http://dx.doi.org/10.1124/dmd.113.055566] [PMID: 24398461]
[11]
Ambudkar, S.V.; Dey, S.; Hrycyna, C.A.; Ramachandra, M.; Pastan, I.; Gottesman, M.M. Biochemical, cellular, and pharmacological aspects of the multidrug transporter. Annu. Rev. Pharmacol. Toxicol., 1999, 39(1), 361-398.
[http://dx.doi.org/10.1146/annurev.pharmtox.39.1.361] [PMID: 10331089]
[12]
Muthusamy, G.; Balupillai, A.; Ramasamy, K.; Shanmugam, M.; Gunaseelan, S.; Mary, B.; Prasad, N.R. Ferulic acid reverses ABCB1-mediated paclitaxel resistance in MDR cell lines. Eur. J. Pharmacol., 2016, 786, 194-203.
[http://dx.doi.org/10.1016/j.ejphar.2016.05.023] [PMID: 27262378]
[13]
Han, Y.; Chin Tan, T.M.; Lim, L.Y. In vitro and in vivo evaluation of the effects of piperine on P-gp function and expression. Toxicol. Appl. Pharmacol., 2008, 230(3), 283-289.
[http://dx.doi.org/10.1016/j.taap.2008.02.026] [PMID: 18417181]
[14]
Silva, R.; Vilas-Boas, V.; Carmo, H.; Dinis-Oliveira, R.J.; Carvalho, F.; de Lourdes Bastos, M.; Remião, F. Modulation of P-glycoprotein efflux pump: Induction and activation as a therapeutic strategy. Pharmacol. Ther., 2015, 149, 1-123.
[http://dx.doi.org/10.1016/j.pharmthera.2014.11.013] [PMID: 25435018]
[15]
Ramos, P.; Schmitz, M.; Gama, S.; Portantiolo, A.; Durruthy, M.G.; de Souza Votto, A.P.; Cornetet, L.R.; Dos Santos Machado, K.; Werhli, A.; Tonel, M.Z.; Fagan, S.B.; Yunes, J.S.; Monserrat, J.M. Cytoprotection of lipoic acid against toxicity induced by saxitoxin in hippocampal cell line HT-22 through in silico modeling and in vitro assays. Pharmacol. Ther., 2018, 393, 171-184.
[http://dx.doi.org/10.1016/j.tox.2017.11.004] [PMID: 29128272]
[16]
Marques, M.B.; de Oliveira, P.V.; Fagan, S.B.; Oliveira, B.R.; da Silva Nornberg, B.F.; Almeida, D.V.; Marins, L.F.; González-Durruthy, M. Modeling drug-drug interactions of AZD1208 with Vincristine and Daunorubicin on ligand-extrusion binding TMD-domains of multidrug resistance P-glycoprotein (ABCB1). Toxicology, 2019, 411, 81-92.
[http://dx.doi.org/10.1016/j.tox.2018.10.009] [PMID: 30339824]
[17]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[18]
Kelley, L.A.; Mezulis, S.; Yates, C.M.; Wass, M.N.; Sternberg, M.J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc., 2015, 10(6), 845-858.
[http://dx.doi.org/10.1038/nprot.2015.053] [PMID: 25950237]
[19]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[20]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[21]
Tao, A.; Huang, Y.; Shinohara, Y.; Caylor, M.L.; Pashikanti, S.; Xu, D. ezCADD: A rapid 2D/3D visualization-enabled web modeling environment for democratizing computer-aided drug design. J. Chem. Inf. Model., 2019, 59(1), 18-24.
[http://dx.doi.org/10.1021/acs.jcim.8b00633] [PMID: 30403855]
[22]
Xie, Z.R.; Hwang, M.J. An interaction-motif-based scoring function for protein-ligand docking. BMC Bioinformatics, 2010, 11(1), 298.
[http://dx.doi.org/10.1186/1471-2105-11-298] [PMID: 20525216]
[23]
Mitternacht, S.; Berezovsky, I.N. Coherent conformational degrees of freedom as a structural basis for allosteric communication. PLOS Comput. Biol., 2011, 7(12), e1002301.
[http://dx.doi.org/10.1371/journal.pcbi.1002301] [PMID: 22174669]
[24]
Keskin, O.; Durell, S.R.; Bahar, I.; Jernigan, R.L.; Covell, D.G. Relating molecular flexibility to function: A case study of tubulin. Biophys. J., 2002, 83(2), 663-680.
[http://dx.doi.org/10.1016/S0006-3495(02)75199-0] [PMID: 12124255]
[25]
Greener, J.G.; Sternberg, M.J. AlloPred: Prediction of allosteric pockets on proteins using normal mode perturbation analysis. BMC Bioinformatics, 2015, 16(1), 335.
[http://dx.doi.org/10.1186/s12859-015-0771-1] [PMID: 26493317]
[26]
Hohenberg, P.; Kohn, W. Inhomogeneous electron gas. Phys. Rev., 1964, 136(3B), 864-871.
[http://dx.doi.org/10.1103/PhysRev.136.B864]
[27]
Kohn, W.; Sham, L.J. Self-consistent equations including exchange and correlation effects. Phys. Rev., 1965, 140(4A), 1133-1138.
[http://dx.doi.org/10.1103/PhysRev.140.A1133]
[28]
Soler, J.M.; Artacho, E.; Gale, J.D.; García, A.; Junquera, J.; Ordejón, P.; Sánchez-Portal, D. The SIESTA method for ab-initio order-N materials simulation. J. Phys. Condens. Matter, 2002, 14(11), 2745-2779.
[http://dx.doi.org/10.1088/0953-8984/14/11/302]
[29]
Troullier, N.; Martins, J.L. Efficient pseudopotentials for plane-wave calculations. Phys. Rev. B Condens. Matter, 1991, 43(3), 1993-2006.
[http://dx.doi.org/10.1103/PhysRevB.43.1993] [PMID: 9997467]
[30]
Perdew, J.P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett., 1996, 77(18), 3865-3868.
[http://dx.doi.org/10.1103/PhysRevLett.77.3865] [PMID: 10062328]
[31]
González-Durruthy, M.; Concu, R.; Vendrame, L.F.O.; Zanella, I.; Ruso, J.M.; Cordeiro, M.N.D.S. Targeting beta-blocker drug-drug interactions with fibrinogen blood plasma protein: A computational and experimental study. Molecules, 2020, 25(22), 5425.
[http://dx.doi.org/10.3390/molecules25225425] [PMID: 33228181]
[32]
Boys, S.F.; Bernardi, F. The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors. Mol. Phys., 1970, 19(4), 553-566.
[http://dx.doi.org/10.1080/00268977000101561]
[33]
Oviedo, M.B.; Wong, B.M. Real-time quantum dynamics reveals complex, many-body interactions in solvated nanodroplets. J. Chem. Theory Comput., 2016, 12(4), 1862-1871.
[http://dx.doi.org/10.1021/acs.jctc.5b01019] [PMID: 26918732]
[34]
Vendrame, L.; Schimtz, B.; Fagan, S.; Zanella, I. Ciclodextrines interacting with methotrexate via molecular modeling. Disciplinarum Sci.: Série. Naturais e Tecnológicas., 2018, 19(3), 401-412.
[35]
Jiménez, J.; Doerr, S.; Martínez-Rosell, G.; Rose, A.S.; De Fabritiis, G. DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 2017, 33(19), 3036-3042.
[http://dx.doi.org/10.1093/bioinformatics/btx350] [PMID: 28575181]
[36]
Chen, V.B.; Arendall, W.B., III; Headd, J.J.; Keedy, D.A.; Immormino, R.M.; Kapral, G.J.; Murray, L.W.; Richardson, J.S.; Richardson, D.C. MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr., 2010, 66(Pt 1), 12-21.
[http://dx.doi.org/10.1107/S0907444909042073] [PMID: 20057044]
[37]
González-Durruthy, M.; Werhli, A.V.; Seus, V.; Machado, K.S.; Pazos, A.; Munteanu, C.R.; González-Díaz, H.; Monserrat, J.M. Decrypting strong and weak single-walled carbon nanotubes interactions with mitochondrial voltage-dependent anion channels using molecular docking and perturbation theory. Sci. Rep., 2017, 7(1), 13271.
[http://dx.doi.org/10.1038/s41598-017-13691-8] [PMID: 29038520]
[38]
Bartosiewicz, D.; Krasowska, A. Inhibitors of ABC transporters and biophysical methods to study their activity. Z. Naturforsch. C J. Biosci., 2009, 64(5-6), 454-458.
[http://dx.doi.org/10.1515/znc-2009-5-625] [PMID: 19678554]

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