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Current Drug Discovery Technologies

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ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

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Research Article

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to Treat Metabolic Syndrome

Author(s): Kirtika Madan*, Sarvesh Paliwal, Swapnil Sharma, Seema Kesar, Neha Chauhan and Mansi Madan

Volume 21, Issue 2, 2024

Published on: 08 September, 2023

Article ID: e010923220643 Pages: 13

DOI: 10.2174/1570163820666230901144003

Price: $65

Abstract

Background: Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism.

Objective: To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome.

Methods: In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor.

Results: The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 μM and 0.0027 μM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed.

Conclusion: The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

Graphical Abstract

[1]
Wakil SJ, Abu-Elheiga LA. Fatty acid metabolism: Target for metabolic syndrome. J Lipid Res 2009; 50: S138-43.
[http://dx.doi.org/10.1194/jlr.R800079-JLR200]
[2]
Seravalle G, Grassi G. Obesity and hypertension. Pharmacol Res 2017; 122: 1-7.
[http://dx.doi.org/10.1016/j.phrs.2017.05.013] [PMID: 28532816]
[3]
Alberti KG, Zimmet P, Shaw J. Metabolic syndrome- a new world-wide definition. A Consensus Statement from the International Diabetes Fedration. Lancet 2006; 366: 1059-62.
[http://dx.doi.org/10.1016/S0140-6736(05)67402-8] [PMID: 16182882]
[4]
Alberti KGMM, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome. Circulation 2009; 120(16): 1640-5.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.109.192644] [PMID: 19805654]
[5]
Alberti KGMM, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation. Diabet Med 1998; 15(7): 539-53.
[http://dx.doi.org/10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S] [PMID: 9686693]
[6]
Salie MJ, Thelen JJ. Regulation and structure of the heteromeric acetyl-CoA carboxylase. Biochim Biophys Acta Mol Cell Biol Lipids 2016; 1861(9): 1207-13.
[http://dx.doi.org/10.1016/j.bbalip.2016.04.004] [PMID: 27091637]
[7]
Lee CK, Cheong HK, Ryu KS, et al. Biotinoyl domain of human acetyl-CoA carboxylase: Structural insights into the carboxyl transfer mechanism. Proteins 2008; 72(2): 613-24.
[http://dx.doi.org/10.1002/prot.21952] [PMID: 18247344]
[8]
Tong L. Acetyl-coenzyme A carboxylase: Crucial metabolic enzyme and attractive target for drug discovery. Cell Mol Life Sci 2005; 62(16): 1784-803.
[http://dx.doi.org/10.1007/s00018-005-5121-4] [PMID: 15968460]
[9]
Harwood HJ Jr. Treating the metabolic syndrome: acetyl-CoA carboxylase inhibition. Expert Opin Ther Targets 2005; 9(2): 267-81.
[http://dx.doi.org/10.1517/14728222.9.2.267] [PMID: 15934915]
[10]
Tong L, Harwood HJ Jr. Acetyl-coenzyme A carboxylases: Versatile targets for drug discovery. J Cell Biochem 2006; 99(6): 1476-88.
[http://dx.doi.org/10.1002/jcb.21077] [PMID: 16983687]
[11]
Stiede K, Miao W, Blanchette HS, et al. Acetyl‐coenzyme A carboxylase inhibition reduces de novo lipogenesis in overweight male subjects: A randomized, double‐blind, crossover study. Hepatology 2017; 66(2): 324-34.
[http://dx.doi.org/10.1002/hep.29246] [PMID: 28470676]
[12]
Bourbeau MP, Bartberger MD. Recent adVances in the development of acetyl Co-A carboxylase (ACC) for the treatment of metabolic syndrome. J Med Chem 2015; 58: 525-36.
[http://dx.doi.org/10.1021/jm500695e] [PMID: 25333641]
[13]
Arya R, Gupta SP, Paliwal S, Kesar S, Mishra A, Prabhakar YS. QSAR and molecular modeling studies on a series of pyrrolidine analogs acting as BACE-1 inhibitors. Lett Drug Des Discov 2019; 16(7): 746-60.
[http://dx.doi.org/10.2174/1570180815666180627124422]
[14]
Kesar S, Paliwal S, Sharma S, et al. In-silico QSAR modelling of predicted Rho Kinase inhibitors against cardio vascular diseases. Curr. Comput-Aid. Drug Des 2019; 15(5): 259-64.
[15]
Gu YG, Weitzberg M, Clark RF, et al. Synthesis and structure-activity relationships of N-{3-[2-(4-Alkoxyphenoxy) thiazol-5-yl]-1-methylprop-2-ynyl}carboxy derivatives as selective acetyl-CoA carboxylase 2 inhibitors. J Med Chem 2006; 49: 3770-3.
[http://dx.doi.org/10.1021/jm060484v] [PMID: 16789734]
[16]
Clark RF, Zhang T, Xin Z, et al. Structure–activity relationships for a novel series of thiazolyl phenyl ether derivatives exhibiting potent and selective acetyl-CoA carboxylase 2 inhibitory activity. Bioorg Med Chem Lett 2006; 16(23): 6078-81.
[http://dx.doi.org/10.1016/j.bmcl.2006.08.100] [PMID: 16973360]
[17]
Clark RF, Zhang T, Wang X, et al. Phenoxy thiazole derivatives as potent and selective acetyl-CoA carboxylase 2 inhibitors: Modulation of isozyme selectivity by incorporation of phenyl ring substituents. Bioorg Med Chem Lett 2007; 17(7): 1961-5.
[18]
Vengurlekar S, Sharma R, Trivedi P. Two- and three-dimensional QSAR studies on benzyl amide-ketoacid inhibitors of HIV integrase and their reduced analogues. Med Chem Res 2010; 19(9): 1106-20.
[http://dx.doi.org/10.1007/s00044-009-9256-y]
[19]
Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 2003; 111(10): 1361-75.
[http://dx.doi.org/10.1289/ehp.5758] [PMID: 12896860]
[20]
Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A. Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 2003; 17(2/4): 241-53.
[http://dx.doi.org/10.1023/A:1025386326946] [PMID: 13677490]
[21]
Gramatica P. Principles of QSAR models validation: Internal and external. QSAR Comb Sci 2007; 26(5): 694-701.
[http://dx.doi.org/10.1002/qsar.200610151]
[22]
Paliwal SK, Verma AN, Paliwal S. Structure–activity relationship analysis of cationic 2-phenylbenzofurans as potent anti-trypanosomal agents: a multivariate statistical approach. Monatsh Chem 2011; 142(10): 1069-86.
[http://dx.doi.org/10.1007/s00706-011-0509-3]
[23]
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 1997; 23(1-3): 3-25.
[http://dx.doi.org/10.1016/S0169-409X(96)00423-1]
[24]
Martínez-Archundia M, Bello M, Correa-Basurto J. Design of drugs by filtering through ADMET, physiochemical and ligand-target flexibility properties. Methods Mol Biol 2018; 1824: 403-16.
[http://dx.doi.org/10.1007/978-1-4939-8630-9_24] [PMID: 30039421]
[25]
Bajusz D, Rácz A, Héberger K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 2015; 7(1): 20.
[http://dx.doi.org/10.1186/s13321-015-0069-3] [PMID: 26052348]
[26]
Cerqueira NMFSA, Gesto D, Oliveira EF, et al. Receptor-based virtual screening protocol for drug discovery. Arch Biochem Biophys 2015; 582: 56-67.
[http://dx.doi.org/10.1016/j.abb.2015.05.011] [PMID: 26045247]
[27]
Madan K, Verma AN, Paliwal SK, Yadav D, Sharma S, Sharma M. Pharmacophore modeling and database mining to identify novel lead compounds active against the disease stage of Trypanosomiasis in the central nervous system. Int J Nutr Pharmacol Neurol Dis 2018; 8(1): 16-31.
[28]
Bello M, Martínez-Archundia M, Correa-Basurto J. Automated docking for novel drug discovery. Expert Opin Drug Discov 2013; 8(7): 821-34.
[http://dx.doi.org/10.1517/17460441.2013.794780] [PMID: 23642085]
[29]
Kesar S, Paliwal S, Mishra P, et al. Identification of Novel Rho-Kinase II inhibitors with vasodilatory activity. ACS Med Chem Lett 2020; 11(9): 1694-703.
[http://dx.doi.org/10.1021/acsmedchemlett.0c00126] [PMID: 32944136]

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