Quantitative Structure-Activity Relationship (QSAR) Studies for the Inhibition of MAOs

Muthusamy	      Ramesh; Arunachalam       Muthuraman

Abstract

Monoamine oxidases are the crucial drug targets for the treatment of neurodegenerative disorders like depression, Parkinson’s disease, and Alzheimer’s disease. The enzymes catalyze the oxidative deamination of several monoamine containing neurotransmitters, i.e. serotonin (5-HT), melatonin, epinephrine, norepinephrine, phenylethylamine, benzylamine, dopamine, tyramine, etc. The oxidative reaction of monoamine oxidases results in the production of hydrogen peroxide that leads to the neurodegeneration process. Therefore, the inhibition of monoamine oxidases has shown a profound effect against neurodegenerative diseases. At present, the design and development of newer lead molecules for the inhibition of monoamine oxidases are under intensive research in the field of medicinal chemistry. Recently, the advancement in QSAR methodologies has shown considerable interest in the development of monoamine oxidase inhibitors. The present review describes the development of QSAR methodologies, and their role in the design of newer monoamine oxidase inhibitors. It will assist the medicinal chemist in the identification of selective and potent monoamine oxidase inhibitors from various chemical scaffolds.

Keywords: Monoamine oxidases, QSAR, Parkinson's disease, Alzheimer's disease, neurodegenerative disorders, monoamine oxidase inhibitors.

[1] 

An introduction to medicinal chemistry, 5th ed; Oxford University Press, 2013. 
[2] 
Verma, J.; Khedkar, V.M.; Coutinho, E.C. 3D-QSAR in drug design-a review. Curr. Top. Med. Chem.,  2010, 10(1), 95-115.
[http://dx.doi.org/10.2174/156802610790232260] [PMID:  19929826] 
[3] 
Kubinyi, H. QSAR: Hansch analysis and related approaches; VCH Publishers: New York, NY, USA, 1993. 
[4] 
Kubinyi, H. Free Wilson analysis. Theory, applications and its relationship to Hansch analysis. Quantitative structure‐activity relationships. Mol. Inform.,  1988, 7, 121-133.
[http://dx.doi.org/10.1002/qsar.19880070303] 
[5] 
Žuvela, P.; David, J.; Wong, M.W. Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids. J. Comput. Chem.,  2018, 39(16), 953-963.
[http://dx.doi.org/10.1002/jcc.25168] [PMID:  29399831] 
[6] 
Ekins, S.; de Groot, M.J.; Jones, J.P. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab. Dispos.,  2001, 29(7), 936-944.
[PMID:  11408357] 
[7] 
Hansch, C.; Maloney, P.P.; Fujita, T.; Muir, R.M. Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature,  1962, 194, 178-180.
[http://dx.doi.org/10.1038/194178b0] 
[8] 
Chohan, K.K.; Paine, S.W.; Waters, N.J. Quantitative structure activity relationships in drug metabolism. Curr. Top. Med. Chem.,  2006, 6(15), 1569-1578.
[9] 
Kubinyi, H. 3D QSAR in Drug Design: Recent Advances; Kluwer Academic Publishers: New York, 2002. 
[10] 
Hansch, C.; Lien, E.J.; Helmer, F. Structure-activity correlations in the metabolism of drugs. Arch. Biochem. Biophys.,  1968, 128(2), 319-330.
[http://dx.doi.org/10.1016/0003-9861(68)90038-6] [PMID:  5698027] 
[11] 
Vaz, R.J.; Nayeem, A.; Santone, K.; Chandrasena, G.; Gavai, A.V. A 3D-QSAR model for CYP2D6 inhibition in the aryloxypropanolamine series. Bioorg. Med. Chem. Lett.,  2005, 15(17), 3816-3820.
[http://dx.doi.org/10.1016/j.bmcl.2005.06.007] [PMID:  15993593] 
[12] 
Cramer, R.D.I.; Patterson, D.E.; Bunce, J.D. ChemInform Abstract: Comparative molecular field analysis (CoMFA). Part 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc.,  1988, 110, 5959-5967.
[http://dx.doi.org/10.1021/ja00226a005] [PMID:  22148765] 
[13] 
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem.,  1994, 37(24), 4130-4146.
[http://dx.doi.org/10.1021/jm00050a010] [PMID:  7990113] 
[14] 
Cruciani, G.; Pastor, M.; Guba, W. VolSurf: a new tool for the pharmacokinetic optimization of lead compounds. Eur. J. Pharm. Sci.,  2000, 11(Suppl. 2), S29-S39.
[http://dx.doi.org/10.1016/S0928-0987(00)00162-7] [PMID:  11033425] 
[15] 
Vepuri, B.S.; Anbazhagan, S.; Naresh, P.; Divya, D. Pharmacophore modeling and docking based qsar studies of aryl amidino isoxazoline derivatives to design potential FXa inhibitors. Am. J. Bioinforma. Res.,  2012, 2, 11-20.
[http://dx.doi.org/10.5923/j.bioinformatics.20120203.01] 
[16] 
Jain, A.N.; Koile, K.; Chapman, D. Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. J. Med. Chem.,  1994, 37(15), 2315-2327.
[http://dx.doi.org/10.1021/jm00041a010] [PMID:  8057280] 
[17] 
Sciabola, S.; Stanton, R.V.; Mills, J.E.; Flocco, M.M.; Baroni, M.; Cruciani, G.; Perruccio, F.; Mason, J.S. High-throughput virtual screening of proteins using GRID molecular interaction fields. J. Chem. Inf. Model.,  2010, 50(1), 155-169.
[http://dx.doi.org/10.1021/ci9003317] [PMID:  19919042] 
[18] 
Breu, B.; Silber, K.; Gohlke, H. Consensus adaptation of fields for molecular comparison (AFMoC) models incorporate ligand and receptor conformational variability into tailor-made scoring functions. J. Chem. Inf. Model.,  2007, 47(6), 2383-2400.
[http://dx.doi.org/10.1021/ci7002472] [PMID:  17958410] 
[19] 
Shulman, K.I.; Herrmann, N.; Walker, S.E. Current place of monoamine oxidase inhibitors in the treatment of depression. CNS Drugs,  2013, 27(10), 789-797.
[http://dx.doi.org/10.1007/s40263-013-0097-3] [PMID:  23934742] 
[20] 
Ramesh, M.; Dokurugu, Y.M.; Thompson, M.D.; Soliman, M.E. Therapeutic, molecular and computational aspects of novel monoamine oxidase (MAO) inhibitors. Comb. Chem. High Throughput Screen.,  2017, 20(6), 492-509.
[http://dx.doi.org/10.2174/1386207320666170310121337] [PMID:  28294055] 
[21] 
Youdim, M.B.H.; Edmondson, D.; Tipton, K.F. The therapeutic potential of monoamine oxidase inhibitors. Nat. Rev. Neurosci.,  2006, 7(4), 295-309.
[http://dx.doi.org/10.1038/nrn1883] [PMID:  16552415] 
[22] 
Mathew, B.; Dev, S.; Suresh, J.; Mathew, G.E.; Lakshmanan, B.; Haridas, A.; Fathima, F.; Krishnan, G.K. Pharmacophore modeling, 3D-QSAR and molecular docking of furanochalcones as inhibitors of monoamine oxidase-B. Cent. Nerv. Syst. Agents Med. Chem.,  2016, 16(2), 105-111.
[http://dx.doi.org/10.2174/1871524915666150319122540] [PMID:  25788143] 
[23] 
Bautista-Aguilera, O.M.; Esteban, G.; Bolea, I.; Nikolic, K.; Agbaba, D.; Moraleda, I.; Iriepa, I.; Samadi, A.; Soriano, E.; Unzeta, M.; Marco-Contelles, J. 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. Eur. J. Med. Chem.,  2014, 75, 82-95.
[http://dx.doi.org/10.1016/j.ejmech.2013.12.028] [PMID:  24530494] 
[24] 
Nikolic, K.; Agbaba, D. Pharmacophore development and SAR studies of imidazoline receptor ligands. Mini Rev. Med. Chem.,  2012, 12(14), 1542-1555.
[http://dx.doi.org/10.2174/138955712803832636] [PMID:  22512575] 
[25] 
Medvedev, A.E.; Ivanov, A.S.; Veselovsky, A.V.; Skvortsov, V.S.; Archakov, A.I. QSAR analysis of indole analogues as monoamine oxidase inhibitors. J. Chem. Inf. Comput. Sci.,  1996, 36(4), 664-671.
[http://dx.doi.org/10.1021/ci950126t] [PMID:  8768761] 
[26] 
Mathew, B.; Adeniyi, A.A.; Dev, S.; Joy, M.; Ucar, G.; Mathew, G.E.; Singh-Pillay, A.; Soliman, M.E. Pharmacophore-based 3D-QSAR analysis of thienyl chalcones as a new class of human MAO-B inhibitors: investigation of combined quantum chemical and molecular dynamics approach. J. Phys. Chem. B,  2017, 121(6), 1186-1203.
[http://dx.doi.org/10.1021/acs.jpcb.6b09451] [PMID:  28084742] 
[27] 
Is, Y.S.; Durdagi, S.; Aksoydan, B.; Yurtsever, M. Proposing novel MAO-B hit inhibitors using multidimensional molecular modeling approaches and application of binary QSAR models for prediction of their therapeutic activity, pharmacokinetic and toxicity properties. ACS Chem. Neurosci.,  2018, 9(7), 1768-1782.
[http://dx.doi.org/10.1021/acschemneuro.8b00095] [PMID:  29671581] 
[28] 
Pathak, A.; Singour, P.K.; Srivastava, A.K.; Gouda, P.; Kumar, S.; Goutam, B.K. Hansch analysis of novel acetamide derivatives as highly potent and specific MAO-A inhibitors. Cent. Nerv. Syst. Agents Med. Chem.,  2016, 16(2), 143-151.
[http://dx.doi.org/10.2174/1871524916666151210143347] [PMID:  26654229] 
[29] 
Pisani, L.; Farina, R.; Nicolotti, O.; Gadaleta, D.; Soto-Otero, R.; Catto, M.; Di Braccio, M.; Mendez-Alvarez, E.; Carotti, A. In silico design of novel 2H-chromen-2-one derivatives as potent and selective MAO-B inhibitors. Eur. J. Med. Chem.,  2015, 89, 98-105.
[http://dx.doi.org/10.1016/j.ejmech.2014.10.029] [PMID:  25462230] 
[30] 
Kneubühler, S.; Thull, U.; Altomare, C.; Carta, V.; Gaillard, P.; Carrupt, P.A.; Carotti, A.; Testa, B. Inhibition of monoamine oxidase-B by 5H-indeno[1,2-c]pyridazines: biological activities, quantitative structure-activity relationships (QSARs) and 3D-QSARs. J. Med. Chem.,  1995, 38(19), 3874-3883.
[http://dx.doi.org/10.1021/jm00019a018] [PMID:  7562919] 
[31] 
Dhiman, P.; Malik, N.; Khatkar, A. 3D-QSAR and in silico studies of natural products and related derivatives as monoamine oxidase inhibitors. Curr. Neuropharmacol.,  2018, 16(6), 881-900.
[http://dx.doi.org/10.2174/1570159X15666171128143650] [PMID: 29189167] 
[32] 
Mladenović, M.; Patsilinakos, A.; Pirolli, A.; Sabatino,  M.; Ragno, R. Understanding the molecular determinant of reversible human monoamine oxidase B inhibitors containing 2H-Chromen-2-One Quantitative Structure-Activity Relationship (QSAR) Combinatorial Chemistry & High Throughput Screening.core: structure-based and ligand-based derived three-dimensional
quantitative structure-activity relationships predictive models.  J. Chem. Inf. Model.,  2017, 57(4), 787-814.
[http://dx.doi.org/10.1021/acs.jcim.6b00608] [PMID:  28291352] 
[33] 
Takao, K.; Yahagi, H.; Uesawa, Y.; Sugita, Y. 3-(E)-Styryl-2H-chromene derivatives as potent and selective monoamine oxidase B inhibitors. Bioorg. Chem.,  2018, 77, 436-442.
[http://dx.doi.org/10.1016/j.bioorg.2018.01.036] [PMID:  29448189] 
[34] 
Santana, L.; Uriarte, E.; González-Díaz, H.; Zagotto, G.; Soto-Otero, R.; Méndez-Alvarez, E. A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. J. Med. Chem.,  2006, 49(3), 1149-1156.
[http://dx.doi.org/10.1021/jm0509849] [PMID:  16451079] 
[35] 
Núñez, M.B.; Maguna, F.P.; Okulik, N.B.; Castro, E.A. QSAR modeling of the MAO inhibitory activity of xanthones derivatives. Bioorg. Med. Chem. Lett.,  2004, 14(22), 5611-5617.
[http://dx.doi.org/10.1016/j.bmcl.2004.08.066] [PMID:  15482934] 
[36] 
Kang, G.I.; Hong, S.K. Quantitative structure-activity relationships in MAO-inhibitory 2-phenylcyclopropylamines: insights into the topography of MAO-A and MAO-B. Arch. Pharm. Res.,  1990, 13, 82-96.
[http://dx.doi.org/10.1007/BF02857840] 

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