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A QSAR Study for the Prediction of Inhibitory Activity of Coumarin Derivatives for the Treatment of Alzheimer’s Disease

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Abstract

The inhibition of acetylcholinesterase (AChE) enzyme has been used as a successful therapeutic strategy for the symptomatic treatment of Alzheimer’s disease and its progression. It is also known that Coumarins, a group of naturally occurring substances in many plants, exhibit a wide range of biological activities such as AChE inhibition. In this study, we present a quantitative structure–activity relationship (QSAR) analysis to predict the inhibitory activity (\({\mathrm{IC}}_{50}\)) of Coumarins derivatives using several statistical regression and machine learning models based on various molecular descriptors of 94 different compounds extracted by the popular Dragon software. The models include multiple linear regression (MLR), partial least squares (PLS), random forests, artificial neural networks, and support vector machine (SVM). Also, a genetic algorithm (GA) was used in combination with MLR, PLS, SVM, and ANN to find a smaller subset of the utilized descriptors. The results indicated that the GA-ANN model achieves the best \({\mathrm{IC}}_{50}\) prediction accuracy.

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References

  1. Pishnamazi, M.; et al.: Measuring solubility of a chemotherapy-anti cancer drug (busulfan) in supercritical carbon dioxide. J. Mol. Liq. 317, 113954, 2020

    Google Scholar 

  2. Walsh, J.P.; Ghadiri, M.; Shirazian, S.: CFD approach for simulation of API release from solid dosage formulations. J. Mol. Liq. 317, 113899, 2020

    Google Scholar 

  3. Zabihi, S.; et al.: Experimental solubility measurements of fenoprofen in supercritical carbon dioxide. J. Chem. Eng. Data 65(4), 1425–1434, 2020

    Google Scholar 

  4. Ismail, H.Y.; et al.: Compartmental approach for modelling twin-screw granulation using population balances. Int. J. Pharm. 576, 118737, 2020

    Google Scholar 

  5. Shirazian, S.; et al.: Multi-dimensional population balance modelling of pharmaceutical formulations for continuous twin-screw wet granulation: determination of liquid distribution. Int. J. Pharm. 566, 352–360, 2019

    Google Scholar 

  6. Ismail, H.Y.; et al.: Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation. Powder Technol. 343, 568–577, 2019

    Google Scholar 

  7. Sajjia, M.; et al.: ANN analysis of a roller compaction process in the pharmaceutical industry. Chem. Eng. Technol. 40(3), 487–492, 2017

    Google Scholar 

  8. Sajjia, M.; et al.: Mechanistic modelling of industrial-scale roller compactor ‘Freund TF-MINI model.’ Comput. Chem. Eng. 104, 141–150, 2017

    Google Scholar 

  9. Rezakazemi, M.; Mosavi, A.; Shirazian, S.: ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 274, 470–476, 2019

    Google Scholar 

  10. Dashti, A.; et al.: Estimating CH4 and CO2 solubilities in ionic liquids using computational intelligence approaches. J. Mol. Liq. 271, 661–669, 2018

    Google Scholar 

  11. Rezakazemi, M.; et al.: Development of hybrid models for prediction of gas permeation through FS/POSS/PDMS nanocomposite membranes. Int. J. Hydrog. Energy 43(36), 17283–17294, 2018

    Google Scholar 

  12. Shirazian, S.; Alibabaei, M.: Using neural networks coupled with particle swarm optimization technique for mathematical modeling of air gap membrane distillation (AGMD) systems for desalination process. Neural Comput. Appl. 28(8), 2099–2104, 2017

    Google Scholar 

  13. Pishnamazi, M.; et al.: Computational fluid dynamics simulation of NO2 molecular sequestration from a gaseous stream using NaOH liquid absorbent through porous membrane contactors. J. Mol. Liq. 313, 113584, 2020

    Google Scholar 

  14. Marjani, A.; et al.: Mass transfer modeling CO2 absorption using nanofluids in porous polymeric membranes. J. Mol. Liq. 2020, 114115, 2020

    Google Scholar 

  15. Babanezhad, M.; et al.: Bubbly flow prediction with randomized neural cells artificial learning and fuzzy systems based on k – ε turbulence and Eulerian model data set. Sci. Rep. 10(1), 13837, 2020

    Google Scholar 

  16. Pishnamazi, M.; et al.: A thermokinetic model for penetrant-induced swelling in polymeric membranes: Water in polybenzimidazole membranes. J. Mol. Liq. 317, 114000, 2020

    Google Scholar 

  17. Pishnamazi, M.; et al.: Molecular investigation into the effect of carbon nanotubes interaction with CO2 in molecular separation using microporous polymeric membranes. Sci. Rep. 10(1), 13285, 2020

    Google Scholar 

  18. Pishnamazi, M.; et al.: Computational investigation on the effect of [Bmim][BF4] ionic liquid addition to MEA alkanolamine absorbent for enhancing CO2 mass transfer inside membranes. J. Mol. Liq. 314, 113635, 2020

    Google Scholar 

  19. Pishnamazi, M.; et al.: Computational study on SO2 molecular separation applying novel EMISE ionic liquid and DMA aromatic amine solution inside microporous membranes. J. Mol. Liq. 313, 113531, 2020

    Google Scholar 

  20. Jooshani, S.; et al.: Contaminant uptake by polymeric passive samplers: a modeling study with experimental validation. Chem. Eng. Res. Des. 129, 231–236, 2018

    Google Scholar 

  21. Khansary, M.A.; Marjani, A.; Shirazian, S.: On the search of rigorous thermo-kinetic model for wet phase inversion technique. J. Membr. Sci. 538, 18–33, 2017

    Google Scholar 

  22. Cao, Y.; et al.: Prediction of fluid pattern in a shear flow on intelligent neural nodes using ANFIS and LBM. Neural Comput. Appl. 32(17), 13313–13321, 2020

    Google Scholar 

  23. Babanezhad, M.; et al.: Developing intelligent algorithm as a machine learning overview over the big data generated by Euler–Euler method to simulate bubble column reactor hydrodynamics. ACS Omega 5(32), 20558–20566, 2020

    Google Scholar 

  24. Nguyen, Q.; et al.: Thermal and flow visualization of a square heat source in a nanofluid material with a cubic-interpolated pseudo-particle. ACS Omega 5(28), 17658–17663, 2020

    Google Scholar 

  25. Babanezhad, M.; Nakhjiri, A.T.; Shirazian, S.: Changes in the number of membership functions for predicting the gas volume fraction in two-phase flow using grid partition clustering of the ANFIS method. ACS Omega 5(26), 16284–16291, 2020

    Google Scholar 

  26. Perkins, R.; et al.: Quantitative structure–activity relationship methods: perspectives on drug discovery and toxicology. Environ. Toxicol. Chem. 22(8), 1666–1679, 2003

    Google Scholar 

  27. Walsh, D.M.; Selkoe, D.J.: Deciphering the molecular basis of memory failure in Alzheimer’s disease. Neuron 44(1), 181–193, 2004

    Google Scholar 

  28. Razavi, S.F.; et al.: Synthesis and evaluation of 4-substituted Coumarins as novel acetylcholinesterase inhibitors. Eur. J. Med. Chem. 64, 252–259, 2013

    Google Scholar 

  29. Selkoe, D.J.; Podlisny, M.B.: Deciphering the genetic basis of Alzheimer’s disease. Annu. Rev. Genom. Hum. Genet. 3, 67–99, 2002

    Google Scholar 

  30. Alvarez, A.; et al.: Acetylcholinesterase, a senile plaque component, affects the fibrillogenesis of amyloid-beta-peptides. Neurosci. Lett. 201(1), 49–52, 1995

    Google Scholar 

  31. Carreiras, M.C.; Marco, J.L.: Recent approaches to novel anti-Alzheimer therapy. Curr. Pharm. Des. 10(25), 3167–3175, 2004

    Google Scholar 

  32. Lleó, A.; Greenberg, S.M.; Growdon, J.H.: Current pharmacotherapy for Alzheimer’s disease. Annu. Rev. Med. 57(1), 513–533, 2006

    Google Scholar 

  33. Lee, S.J.; et al.: Inhibitory effect of esculetin on migration, invasion and matrix metalloproteinase-9 expression in TNF-α-induced vascular smooth muscle cells. Mol. Med. Rep. 4(2), 337–341, 2011

    Google Scholar 

  34. Huang, X.Y.; et al.: Study on the anticancer activity of Coumarin derivatives by molecular modeling. Chem. Biol. Drug Des. 78(4), 651–658, 2011

    Google Scholar 

  35. Bahadır, O.; et al.: Hepatoprotective and TNF-α inhibitory activity of Zosima absinthifolia extracts and Coumarins. Fitoterapia 82(3), 454–459, 2011

    Google Scholar 

  36. Huang, L.; et al.: Mechanism of action and resistant profile of anti-HIV-1 Coumarin derivatives. Virology 332(2), 623–628, 2005

    Google Scholar 

  37. Hwu, J.R.; et al.: Synthesis of new benzimidazole-Coumarin conjugates as anti-hepatitis C virus agents. Antiviral Res. 77(2), 157–162, 2008

    Google Scholar 

  38. Kumar, R.; Saha, A.; Saha, D.: A new antifungal Coumarin from Clausena excavata. Fitoterapia 83(1), 230–233, 2012

    Google Scholar 

  39. Arshad, A.; et al.: Synthesis and antimicrobial properties of some new thiazolyl Coumarin derivatives. Eur. J. Med. Chem. 46(9), 3788–3794, 2011

    Google Scholar 

  40. Roussaki, M.; et al.: A novel synthesis of 3-aryl Coumarins and evaluation of their antioxidant and lipoxygenase inhibitory activity. Bioorg. Med. Chem. Lett. 20(13), 3889–3892, 2010

    Google Scholar 

  41. Sashidhara, K.V.; et al.: Discovery and synthesis of novel 3-phenylcoumarin derivatives as antidepressant agents. Bioorg. Med. Chem. Lett. 21(7), 1937–1941, 2011

    Google Scholar 

  42. Fallarero, A.; et al.: Inhibition of acetylcholinesterase by Coumarins: the case of Coumarin 106. Pharmacol. Res. 58(3–4), 215–221, 2008

    Google Scholar 

  43. Shen, Q.; et al.: Synthesis and biological evaluation of functionalized Coumarins as acetylcholinesterase inhibitors. Eur. J. Med. Chem. 40(12), 1307–1315, 2005

    Google Scholar 

  44. Soto-Ortega, D.D.; et al.: Inhibition of amyloid-β aggregation by Coumarin analogs can be manipulated by functionalization of the aromatic center. Bioorg. Med. Chem. 19(8), 2596–2602, 2011

    Google Scholar 

  45. Zhou, X.; et al.: Design, synthesis, and acetylcholinesterase inhibitory activity of novel Coumarin analogues. Bioorg. Med. Chem. 16(17), 8011–8021, 2008

    Google Scholar 

  46. Alipour, M.; et al.: Novel Coumarin derivatives bearing N-benzyl pyridinium moiety: potent and dual binding site acetylcholinesterase inhibitors. Bioorg. Med. Chem. 20(24), 7214–7222, 2012

    Google Scholar 

  47. Abbott, M.L.: Introduction to multiple linear regression. In: Using Statistics in the Social and Health Sciences with SPSS® and Excel®, pp. 417–454 (2016)

  48. Vinzi, V.E.: Handbook of partial least squares. In: Vinzi, V.E., et al. (eds.) Springer Handbooks of Computational Statistics. Springer, Berlin (2013)

    Google Scholar 

  49. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32, 2001

    MATH  Google Scholar 

  50. Jain, A.K.; Mao, J.C.; Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31, 1996

    Google Scholar 

  51. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167, 1998

    Google Scholar 

  52. Dragon software version 2.1. https://www.vcclab.org/lab/edragon/. Accessed: 05 Jan 2018

  53. Deb, K.: An introduction to genetic algorithms. Sadhana 24(4), 293–315, 1999

    MathSciNet  MATH  Google Scholar 

  54. Dragon molecular descriptor list. https://www.talete.mi.it/products/dragon_description.htm. Accessed: 05 Jan 2018

  55. Libsvm—a library for support vector machines. https://www.csie.ntu.edu.tw/cjlin/libsvm/. Accessed: 05 Jan 2018

  56. Ellman, G.L.; et al.: A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 7(2), 88–000, 1961

    Google Scholar 

  57. Hadizadeh, F.; Vahdani, S.; Jafarpour, M.: Quantitative structure–activity relationship studies of 4-imidazolyl-1,4-dihydropyridines as calcium channel blockers. Iran. J. Basic Med. Sci. 16(8), 910–916, 2013

    Google Scholar 

  58. Clark, M.; Cramer, R.D.; Vanopdenbosch, N.: Validation of the general-purpose tripos 52 force-field. J. Comput. Chem. 10(8), 982–1012, 1989

    Google Scholar 

  59. Rostami, A.; Baghban, A.; Shirazian, S.: On the evaluation of density of ionic liquids: towards a comparative study. Chem. Eng. Res. Des. 147, 648–663, 2019

    Google Scholar 

  60. Devillers, J.: 1—strengths and weaknesses of the backpropagation neural network in QSAR and QSPR studies. In: Devillers, J. (ed.) Neural Networks in QSAR and Drug Design, pp. 1–46. Academic Press, London (1996)

    Google Scholar 

  61. Zabihi, S.; et al.: Development of hybrid ANFIS-CFD model for design and optimization of membrane separation of benzoic acid. J. Nonequilib. Thermodyn. 44(3), 285–293, 2019

    MathSciNet  Google Scholar 

  62. Ismail, H.Y.; et al.: ANN-Kriging hybrid model for predicting carbon and inorganic phosphorus recovery in hydrothermal carbonization. Waste Manag. 85, 242–252, 2019

    Google Scholar 

  63. Bishop, C.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2011)

    Google Scholar 

  64. Sahigara, F.; et al.: Assessing the validity of QSARs for ready biodegradability of chemicals: an applicability domain perspective. Curr. Comput. Aided Drug Des. 10(2), 137–147, 2014

    Google Scholar 

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Authors and Affiliations

  1. Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran

    Sarah Ghanei-Nasab & Azam Marjani

  2. Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

    Farzin Hadizadeh

  3. Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medicinal Sciences, Tehran, Iran

    Alireza Foroumadi

Authors
  1. Sarah Ghanei-Nasab
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  2. Farzin Hadizadeh
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  4. Azam Marjani
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Correspondence to Farzin Hadizadeh.

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Ghanei-Nasab, S., Hadizadeh, F., Foroumadi, A. et al. A QSAR Study for the Prediction of Inhibitory Activity of Coumarin Derivatives for the Treatment of Alzheimer’s Disease. Arab J Sci Eng 46, 5523–5531 (2021). https://doi.org/10.1007/s13369-020-05064-7

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