Abstract
Background: Design of novel PPAR-γ modulators with better binding efficiency and fewer side effects to treat type 2 diabetes is still a challenge for medicinal chemists. Cost and time efficient computational methods have presently become an integral part of research in nuclear receptors and their ligands, enabling hit to lead identification and lead optimization. This review will focus on cutting-edge technologies used in most recent studies on the design of PPAR- γ agonists and will discuss the chemistry of few molecules which emerged successful.
Methods: Literature review was carried out in google scholar using customized search from 2011- 2017. Computer-aided design methods presented in this article were used as search terms to retrieve corresponding literature.
Results: Virtual screening of natural product libraries is an effective strategy to harness nature as the source of ligands for PPARs. Rigid and induced fit docking and core hopping approach in docking are rapid screening methods to predict the PPAR- γ and PPAR-α/ γ dual agonistic activity. Onedimensional drug profile matching is one of the recent virtual screening methods by which an antiprotozoal drug, Nitazoxanide was identified as a PPAR- γ agonist.
Conclusion: It is concluded that to achieve a convincing and reliable design of PPAR-γ agonist by virtual screening techniques, customized workflow comprising of appropriate models is essential in which methods may be applied either sequentially or simultaneously.
Keywords: Virtual screening, PPAR- γ agonists, type 2 diabetes, natural product ligands, pharmacophore modelling, docking, core hopping, drug profile matching.
Graphical Abstract
[1]
Deepanwita M, Samanta S. A review on the role of peroxisome prolifertor-activated receptor-γ agonists and hybrids in type 2 diabetes and cardiomyopathy. Asian J Pharm Clin Res 2015; 8: 974-2441.
[2]
Chandra V, Huang P, Hamuro Y, et al. Structure of the intact PPAR-γ–RXR-α nuclear receptor complex on DNA. Nature 2008; 456(7220): 350.
[3]
Grygiel-Górniak B. Peroxisome proliferator-activated receptors and their ligands: Nutritional and clinical implications-a review. Nutr J 2014; 13(1): 17.
[4]
Thangavel N, Al Bratty M, Akhtar Javed S, Ahsan W, Alhazmi HA. Targeting peroxisome proliferator-activated receptors using thiazolidinediones: Strategy for design of novel antidiabetic drugs. Int J Med Chem 2017; 2017: 1069718.
[5]
Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J Org Chem 2016; 12: 2694-718.
[6]
Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev 2014; 66(1): 334-95.
[7]
Ai N, Krasowski MD, Welsh WJ, Ekins S. Understanding nuclear receptors using computational methods. Drug Discov Today 2009; 14(9): 486-94.
[8]
Montanari R, Saccoccia F, Scotti E, et al. Crystal structure of the peroxisome proliferator-activated receptor γ (PPARγ) ligand binding domain complexed with a novel partial agonist: A new region of the hydrophobic pocket could be exploited for drug design. J Med Chem 2008; 51(24): 7768-76.
[9]
Schwarz R, Tänzler D, Ihling CH, Müller MQ, Kölbel K, Sinz A. Monitoring conformational changes in peroxisome proliferator-activated receptor α by a genetically encoded photoamino acid, cross-linking, and mass spectrometry. J Med Chem 2013; 56(11): 4252-63.
[10]
Schwarz R, Tänzler D, Ihling CH, Sinz A. Monitoring solution structures of peroxisome proliferator-activated receptor β/δ upon ligand binding. PLoS One 2016; 11(3): e0151412.
[11]
Tsakovska I, Al Sharif M, Alov P, et al. Molecular modelling study of the PPARγ receptor in relation to the mode of action/adverse outcome pathway framework for liver steatosis. Int J Mol Sci 2014; 15(5): 7651-66.
[12]
Dixit VA, Bharatam PV. SAR and computer-aided drug design approaches in the discovery of peroxisome proliferator-activated receptor γ activators: a perspective. J Comput Med 2013; 2013: 406049.
[13]
de Groot JC, Weidner C, Krausze J, et al. Structural characterization of amorfrutins bound to the peroxisome proliferator-activated receptor γ. J Med Chem 2013; 56(4): 1535-43.
[14]
Guasch L, Sala E, Valls C, et al. Structural insights for the design of new PPARgamma partial agonists with high binding affinity and low transactivation activity. J Comput Aided Mol Des 2011; 25(8): 717-28.
[15]
Wang L, Waltenberger B, Pferschy-Wenzig E-M, et al. Natural product agonists of peroxisome proliferator-activated receptor gamma (PPARγ): a review. Biochem Pharmacol 2014; 92(1): 73-89.
[16]
Sharma V, Sarkar IN. Bioinformatics opportunities for identification and study of medicinal plants. Brief Bioinform 2012; 14(2): 238-50.
[17]
Medina-Franco JL. Advances in computational approaches for drug discovery based on natural products. Rev Latinoam Quím 2013; 41(2): 95-110.
[18]
Yap CW. PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem 2011; 32(7): 1466-74.
[19]
Guasch L, Sala E, Castell-Auví A, et al. Identification of PPARgamma partial agonists of natural origin (I): Development of a virtual screening procedure and in vitro validation. PLoS One 2012; 7(11): e50816.
[20]
Muralikumar S, Vetrivel U, Narayanasamy A, Das UN. Probing the intermolecular interactions of PPARγ-LBD with polyunsaturated fatty acids and their anti-inflammatory metabolites to infer most potential binding moieties. Lipids Health Dis 2017; 16(1): 17.
[21]
El-Houri RB, Mortier J, Murgueitio MS, Wolber G, Christensen LP. Identification of PPARγ agonists from natural sources using different in silico approaches. Planta Med 2015; 81(06): 488-94.
[22]
Yang S-Y. Pharmacophore modelling and applications in drug discovery: challenges and recent advances. Drug Discov Today 2010; 15(11): 444-50.
[23]
Agrawal R, Jain PN, Dikshit S. Ligand-based pharmacophore detection and screening of potential glitazones. Curr Enzym Inhib 2012; 8(1): 22-46.
[24]
Kaserer T, Obermoser V, Weninger A, Gust R, Schuster D. Evaluation of selected 3D virtual screening tools for the prospective identification of peroxisome proliferator-activated receptor (PPAR) γ partial agonists. Eur J Med Chem 2016; 124: 49-62.
[25]
Sohn Y-S, Park C, Lee Y, et al. Multi-conformation dynamic pharmacophore modelling of the peroxisome proliferator-activated receptor γ for the discovery of novel agonists. J Mol Graph Model 2013; 46: 1-9.
[26]
Lu I-L, Huang C-F, Peng Y-H, et al. Structure-based drug design of a novel family of PPARγ partial agonists: virtual screening, X-ray crystallography, and in vitro/in vivo biological activities. J Med Chem 2006; 49(9): 2703-12.
[27]
Chen K-C, Chang S-S, Huang H-J, Lin T-L, Wu Y-J, Chen CY-C. Three-in-one agonists for PPAR-α, PPAR-γ, and PPAR-δ from traditional Chinese medicine. J Biomol Struct Dyn 2012; 30(6): 662-83.
[28]
Wieder M, Perricone U, Boresch S, Seidel T, Langer T. Evaluating the stability of pharmacophore features using molecular dynamics simulations. Biochem Biophys Res Commun 2016; 470(3): 685-9.
[29]
Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: A problem-centric review. AAPS J 2012; 14(1): 133-41.
[30]
Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules 2015; 20(7): 13384-421.
[31]
Meng X-Y, Zhang H-X, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 2011; 7(2): 146-57.
[32]
Encinar JA, Fernández-Ballester G, Galiano-Ibarra V, Micol V. In silico approach for the discovery of new PPARγ modulators among plant-derived polyphenols. Drug Des Devel Ther 2015; 9: 5877.
[33]
Nazreen S, Alam MS, Hamid H, et al. Design, synthesis, in silico molecular docking and biological evaluation of novel oxadiazole based thiazolidine-2, 4-diones bis-heterocycles as PPAR-γ agonists. Eur J Med Chem 2014; 87: 175-85.
[34]
Nazreen S, Alam MS, Hamid H, et al. Thiazolidine-2, 4-diones derivatives as PPAR-γ agonists: Synthesis, molecular docking, in vitro and in vivo antidiabetic activity with hepatotoxicity risk evaluation and effect on PPAR-γ gene expression. Bioorg Med Chem Lett 2014; 24(14): 3034-42.
[35]
Priyadarsini R, Durga V, Ahmed S. Virtual screening, synthesis of newer heterocycles as PPAR -gamma agonists with antidiabetic activity. Int J Pharm Sci Res 2017; 8(2): 631.
[36]
Gaddipati R, Raikundalia GK, Mathai ML. Comparison of autodock and glide towards the discovery of PPAR agonists. Int J Biosci Biochem Bioinform 2014; 4(2): 100.
[37]
Nabuurs SB, Wagener M, De Vlieg J. A flexible approach to induced fit docking. J Med Chem 2007; 50(26): 6507-18.
[38]
Mannhold R, Kubinyi H, Folkers G. Virtual screening: principles, challenges, and practical guidelines. John Wiley & Sons 2011.
[39]
Muñoz-Gutierrez C, Adasme-Carreño F, Fuentes E, Palomo I, Caballero J. Computational study of the binding orientation and affinity of PPARγ agonists: Inclusion of ligand-induced fit by cross-docking. RSC Advances 2016; 6(69): 64756-68.
[40]
Bajorath J. Computational scaffold hopping: Cornerstone for the future of drug design? Future Med Chem 2017; 9(7): 629-31.
[41]
Ma Y, Wang S-Q, Xu W-R, Wang R-L, Chou K-C. Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach. PLoS One 2012; 7(6): e38546.
[42]
Wang X-J, Zhang J, Wang S-Q, Xu W-R, Cheng X-C, Wang R-L. Identification of novel multitargeted PPARα/γ/δ pan agonists by core hopping of rosiglitazone. Drug Des Devel Ther 2014; 8: 2255.
[43]
Dilly SJ, Morris GS. Pimping up drugs recovered, superannuated and under exploited drugs-an introduction to the basics of drug reprofiling. Curr Drug Discov Technol 2017; 14(2): 121-6.
Article Metrics
44
5