Relational design of Alzheimer's disease drugs: QSAR and molecular docking studies

Authors

  • Alberto Bencomo-Martínez Centro de Neurociencias de Cuba, La Habana, Cuba
  • Yoanna María Álvarez-Ginarte Laboratorio de Química Computacional y Teórica, Universidad de la Habana, La Habana, Cuba
  • Ayron Thomas Sánchez-Tamayo Centro de Matemática Computacional, Universidad de las Ciencias Informáticas, La Habana, Cuba
  • Marquiza Sablón-Carrazana Centro de Neurociencias de Cuba, La Habana, Cuba
  • Chryslaine Rodríguez-Tanty Centro de Neurociencias de Cuba, La Habana, Cuba

Keywords:

Alzheimer's disease; acetylcholinesterase; β-amyloid peptide; QSAR; molecular docking.

Abstract

La enfermedad de Alzheimer (EA) es un trastorno neurológico caracterizado por la pérdida progresiva de la memoria, debido a procesos bioquímicos de origen multifactorial. Esto conduce a la disrupción de la sinapsis neuronal, y como consecuencia, a la demencia. En el presente trabajo se realizaron estudios de relación estructura-actividad y de acoplamiento molecular para dos series de compuestos con actividad biológica in vitro frente a dos blancos terapéuticos para la EA: con efecto antiagregante del péptido β-amiloide y con actividad inhibidora de la enzima acetilcolinesterasa. Se obtuvo un modelo significativo y predictivo de dependencia de la actividad inhibitoria al 50 % (IC50) para cada serie. Los resultados de acoplamiento molecular predicen un posible modo de interacción de los compuestos con cada blanco terapéutico estudiado.

References

- PRINCE, M. y otros. “Recent Global Trends in the Prevalence and Incidence of Dementia, and Survival with Dementia.” Alzheimer’s Research & Therapy. 2016, 8 (1), 23. DOI:10.1186/s13195-016-0188-8.

- REYMOND, V. y otros. “Percepción de las demencias y la intersectorialidad en el contexto del Policlínico Docente Playa”. Revista Cubana de Salud Pública. 2019, 45 (1):1-20. Disponible en: <http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-34662019000100011&lng=es&nrm=iso. ISSN 1561-3127.

- PROFENNO, L. A.; PORSTEINSSON, A. P.; FARAONE, S. V. “Meta-Analysis of Alzheimer’s Disease Risk with Obesity, Diabetes, and Related Disorders.” Biological Psychiatry. 2010, 67 (6), 505–512. DOI:10.1016/j.biopsych.2009.02.013.

- ZHAO, W. Q.; TOWNSEND, M. “Insulin Resistance and Amyloidogenesis as Common Molecular Foundation for Type 2 Diabetes and Alzheimer’s Disease.” Biochimica et Biophysica Acta - Molecular Basis of Disease. 2009, 1792 (5), 482–496. DOI:10.1016/j.bbadis.2008.10.014.

- LUHRS, T. y otros. “3D structure of Alzheimer's amyloid-beta(1-42) fibrils.” Proceedings of the National Academy of Sciences of the United States of America. 2005, 102 (48), 17342-17347. DOI:10.1073/pnas.0506723102.

- BRÄNNSTRÖM, K. y otros. “The N-terminal region of amyloid β controls the aggregation rate and fibril stability at low pH through a gain of function mechanism.” Journal of the American Chemical Society. 2014, 136 (31), 10956-10964. DOI:

1021/ja503535m.

- CONGDON, E. E.; SIGURDSSON, E. M. “Tau-Targeting Therapies for Alzheimer Disease.” Nature Reviews Neurology. 2018, 14 (7), 399–415. DOI:10.1038/s41582-018-0013-z.

- JOUANNE, M.; RAULT, S.; VOISIN-CHIRET, A. S. “Tau Protein Aggregation in Alzheimer’s Disease: An Attractive Target for the Development of Novel Therapeutic Agents.” European Journal of Medicinal Chemistry. 2017, 139, 153–167. DOI:10.1016/j.ejmech.2017.07.070.

- HAMPEL, H. y otros. “Revisiting the Cholinergic Hypothesis in Alzheimer’s Disease: Emerging Evidence from Translational and Clinical Research.” Alzheimer’s & Dementia. 2017, 1–14. DOI:10.1016/j.jalz.2017.08.016.

- REITZ, C. “Alzheimer’s Disease and the Amyloid Cascade Hypothesis: A Critical Review.” International Journal of Alzheimer’s Disease. 2012, 2012, 1–11. DOI:10.1038/nrd3505.

- CRESCENZI, O. y otros. “Solution Structure of the Alzheimer Amyloid β-Peptide (1-42) in an Apolar Microenvironment.” European Journal of Biochemistry. 2002, 269 (22), 5642–5648. DOI:10.1046/j.1432-1033.2002.03271.x.

- KRYGER, G.; SILMAN, I.; SUSSMAN, J. L. “Structure of Acetylcholinesterase Complexed with E2020 (Ariceptρ): Implications for the Design of New Anti-Alzheimer Drugs.” Structure. 1999, 7 (3), 297–307. DOI:10.1016/S0969-2126(99)80040-9.

- TOLAR, M. y otros. “Aducanumab, gantenerumab, BAN2401, and ALZ-801—the first wave of amyloid-targeting drugs for Alzheimer’s disease with potential for near term approval.” Alzheimer's Research & Therapy. 2020, 12 (1), 95-100. DOI:10.1186/s13195-020-00663-w.

- AZAM, F. y otros. “NSAIDs as Potential Treatment Option for Preventing Amyloid β Toxicity in Alzheimer’s Disease: An Investigation by Docking, Molecular Dynamics, and DFT Studies.” Journal of Biomolecular Structure and Dynamics. 2018, 36 (8), 2099–2117. DOI:10.1080/07391102.2017.1338164.

- CAVDAR, H. y otros. “Inhibition of Acetylcholinesterase and Butyrylcholinesterase with Uracil Derivatives: Kinetic and Computational Studies.” Journal of Enzyme Inhibition and Medicinal Chemistry. 2019, 34 (1), 429–437. DOI:10.1080/14756366.2018.1543288.

- KENOUCH, S. y otros. “Predictive QSAR Model and Clustering Analysis of Some Benzothiazole Derivatives as Cytotoxic Inhibitors.” SDRP Journal of Computational Chemistry & Molecular Modelling. 2018, 2 (3), 1–8. DOI:10.25177/JCCMM.2.3.3.

- HANWELL, M. D. y otros. “Avogadro: An Advanced Semantic Chemical Editor, Visualization, and Analysis Platform.” Journal of Cheminformatics. 2012, 4 (8) DOI:10.1186/1758-2946-4-17.

- MITIN, A. V. BAKER, J.; PULAY, P. “An Improved 6-31G* Basis Set for First-Row Transition Metals.” The Journal of Chemical Physics. 2003, 118 (17), 7775–7782. DOI:10.1021/jp102712z.

- CIVALLERI, B. y otros. “B3LYP Augmented with an Empirical Dispersion Term (B3LYP-D*) as Applied to Molecular Crystals.” CrystEngComm. 2008, 10 (4), 405–410. DOI:10.1039/b715018k.

- HYPERCUBE. Hyper Chem Release 7; 2002. Disponible en: www.chemistry-software.com

- HOWLETT, D. R. y otros. “Inhibition of Fibril Formation in β-Amyloid Peptide by a Novel Series of Benzofurans.” Biochemical Journal. 1999, 340 (1), 283. DOI:10.1042/0264-6021:3400283.

- RE, F. y otros. “Beta Amyloid Aggregation Inhibitors: Small Molecules as Candidate Drugs for Therapy of Alzheimers Disease.” Current Medicinal Chemistry. 2010, 17 (27), 2990–3006. DOI:10.2174/092986710791959729.

- MATSUZAKI, K. y otros. “Inhibitors of Amyloid β-Protein Aggregation Mediated by GM1-Containing Raft-like Membranes.” Biochimicaet Biophysica Acta (BBA) - Biomembranes. 2007, 1768 (1), 122–130. DOI:10.1016/j.bbamem.2006.09.014.

- BAUTISTA-AGUILERA, O. y otros. “Design, Synthesis, Pharmacological Evaluation, QSAR Analysis, Molecular Modeling and ADMET of Novel Donepezil–Indolyl Hybrids as Multipotent Cholinesterase/Monoamine Oxidase Inhibitors for the Potential Treatment of Alzheimer’s Disease.” European Journal of Medicinal Chemistry. 2014, 75, 82–95. DOI:10.1016/j.ejmech.2013.12.028.

- BOLEA, I. y otros. “Synthesis, Biological Evaluation, and Molecular Modeling of Donepezil and N -[(5-(Benzyloxy)-1-Methyl-1 H -Indol-2-Yl)Methyl]- N -Methylprop-2-Yn-1-Amine Hybrids as New Multipotent Cholinesterase/Monoamine Oxidase Inhibitors for the Treatment of Alzheimer.” Journal of Medicinal Chemistry. 2011, 54 (24), 8251–8270. DOI:10.1021/jm200853t.

- DE OLIVEIRA, D. B.; GAUDIO, A. C. “Build QSAR: A New Computer Program for QSAR Analysis.” Quantitative Structure-Activity Relationships. 2000, 19 (6), 599–601. DOI:10.1002/1521-3838(200012)19:6<599::AID-QSAR599>3.0.CO;2-B.

- HILBE, J. M. “STATISTICA 7.” The American Statistician. 2007, 61 (1), 91–94. DOI:10.1198/000313007X172998.

- PIRHADI, S.; SHIRI, F.; GHASEMI, J. B. “Multivariate Statistical Analysis Methods in QSAR.” RSC Advances. 2015, 5 (127), 104635–104665. DOI:10.1039/c5ra10729f.

- JAKALIAN, A.; JACK, D. B.; BAYLY, C. I. “Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation.” Journal of Computational Chemistry. 2002, 23 (16), 1623–1641. ISSN: 1096-987X. DOI:01928651.

- BURGIN, S. R. y otros. “High School Biology Students Use of Visual Molecular Dynamics as an Authentic Tool for Learning about Modeling as a Professional Scientific Practice.” Biochemistry and Molecular Biology Education. 2018, 46 (3), 230–236. DOI:10.1002/bmb.21113.

- TROTT, O.; OLSON, A. J. “Auto Dock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading.” Journal of Computational Chemistry. 2009, 32, NA-NA. DOI:10.1002/jcc.21334.

- MORRIS, G. M. y otros. “AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility.” Journal of Computational Chemistry. 2009, 30 (16), 2785–2791. DOI:10.1002/jcc.21256.

BASSI, S. “A Primer on Python for Life Science Researchers.” PLoS Computational Biology. 2007, 3 (11), 2052–2057. DOI:10.1371/journal.pcbi.0030199.

- Rosenfeld, R. J. y otros. “Automated Docking of Ligands to an Artificial Active Site: Augmenting Crystallographic Analysis with Computer Modeling.” Journal of Computer-Aided Molecular Design. 2003, 17(8), 525–536. DOI:10.1023/B:JCAM.0000004604.87558.02.

- GHANEI-NASAB, S. y otros. “A QSAR Study for the Prediction of Inhibitory Activity of Coumarin Derivatives for the Treatment of Alzheimer’s Disease.” Arabian Journal for Science and Engineering. 2021, 46 (6), 5523–5531. DOI:10.1007/s13369-020-05064-7.

- PETER, S. C. y otros. “Quantitative Structure-Activity Relationship (QSAR): Modeling Approaches to Biological Applications.” In Encyclopedia of Bioinformatics and Computational Biology; Elsevier, 2019; 1–3, pp 661–676. DOI:10.1016/B978-0-12-809633-8.20197-0

- KUMER, A.; SARKER, M. N.; PAUL, S. “The Theoretical Investigation of HOMO, LUMO, Thermophysical Properties and QSAR Study of Some Aromatic Carboxylic Acids Using Hyper Chem Programming.” International Journal of Chemistry and Technology. 2019, 3 (1), 26–37. DOI:10.32571/ijct.478179.

- GU, W.; LI, Q.; LI, Y. “Environment-Friendly PCN Derivatives Design and Environmental Behavior Simulation Based on a Multi-Activity 3D-QSAR Model and Molecular Dynamics.” Journal of Hazardous Materials. 2020, 393 (November 2019), 122339. DOI:10.1016/j.jhazmat.2020.122339.

- LIPINSKI, C. A. y otros. “Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings.” Advanced Drug Delivery Reviews. 1997, 23 (1–3), 3–25. DOI:10.1016/S0169-409X(96)00423-1.

- ZEWOTIR, T.; GALPIN, J. S. “A Unified Approach on Residuals, Leverages and Outliers in the Linear Mixed Model.” TEST. 2007, 16 (1), 58–75. DOI:10.1007/s11749-006-0001-2.

- SABLÓN-CARRAZANA, M. y otros. “Drug Development in Conformational Diseases: A Novel Family of Chemical Chaperones That Bind and Stabilise Several Polymorphic Amyloid Structures.” PLOS ONE. 2015, 10 (9), e0135292. DOI:10.1371/journal.pone.0135292.

Downloads

Published

2022-09-05

How to Cite

Bencomo-Martínez, A. ., Álvarez-Ginarte, Y. M. ., Sánchez-Tamayo, A. T. ., Sablón-Carrazana, M. ., & Rodríguez-Tanty, C. . (2022). Relational design of Alzheimer’s disease drugs: QSAR and molecular docking studies. Revista Cubana De Química, 34(3), 369–402. Retrieved from https://cubanaquimica.uo.edu.cu/index.php/cq/article/view/5265

Issue

Vol. 34 No. 3 (2022)

Section

Artículos

License

Copyright (c) 2022 Alberto Bencomo-Martínez, Yoanna María Álvarez-Ginarte, Ayron Thomas Sánchez-Tamayo, Marquiza Sablón-Carrazana, Chryslaine Rodríguez-Tanty

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This journal provides immediate open access to its content, based on the principle that offering the public free access to research helps a greater global exchange of knowledge. Each author is responsible for the content of each of their articles. 

Most read articles by the same author(s)