QSAR studies on bisaryl substituted thiazoles and oxazoles as PPARδ agonists

1. Available on line www.jocpr.com Journal of Chemical and Pharmaceutical Research __________________________________________________ J. Chem. Pharm. Res., 2011, 3(2):792-801 ISSN No: 0975-7384 CODEN(USA): JCPRC5 QSAR studies on bisaryl substituted thiazoles and oxazoles as PPARδ agonists A. Vasudeva Rao*, G. Naga Sandhya, S. Arun Sathya Dev and Y. Rajendra Prasad Pharmaceutical Chemistry Division, A U College of Pharmaceutical Sciences, Andhra University, Visakhapatnam, Andhra Pradesh, India ______________________________________________________________________________ ABSTRACT A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and predicting the agonistic activity of PPARδ receptor. The model was produced by using the multiple linear regression (MLR) technique on a compound database that consists of newly discovered bisaryl substituted thiazoles and oxazoles. The major conclusion of this study is that molecular weight, wiener index, andrews affinity and polar surface area affect significantly the agonistic activity of PPARδ receptor by bisaryl substituted thiazoles and oxazoles. The selected QSAR descriptors serve as a primary guidance for the design of novel and selective PPARδ receptor agonists. Key Words: PPARδ, QSAR ______________________________________________________________________________ INTRODUCTION Peroxisome proliferator activated receptors (PPARs) are important members of the nuclear hormone receptor superfamily. These receptors are ligand activated transcription factors known to play a key role in the catabolism and storage of dietary fats [1]. Three PPAR isotypes: PPARα (NR1C1), PPARδ (also called β {NR1C2}), PPARγ (NR1C3) have been identified so far. Once activated by their respective ligand, PPARs control transcriptional rate of a large panel of genes implicated in various physiological functions, including lipid and glucose homeostasis, inflammation, cell proliferation and differentiation. The various PPAR isotypes have different physiological roles [2]. Recently, several synthetic ligands have been reported to selectively 792

2. A. Vasudeva Rao et al J. Chem. Pharm. Res., 2011, 3(2):792-801 ______________________________________________________________________________ activate PPARδ include 3,4,5-trisubstituted isoxazoles [3], 1,3,5-trisubstituted aryls [4], benzothiophenes, benzofuran and indole based compounds [5], anthranilic acid GW9371 [6], phenylpropanoic acid derivatives bearing 6-substituted benzothiazoles [7], para-alkyl thio phenoxy aceticacids [8]. The phenoxy acetic acid derivatives GW501516 and GW0742 are the highly selective PPARδ ligands with nanomolar affinity and 1000-fold selectivity over other isotypes PPARα and PPARγ [9]. Novel bisaryl substituted thiazoles and oxazoles are highly potent and selective PPARδ agonists [10]. The other PPARδ agonists L796449, L165461 [11], KD3010 and MBX-8025 are currently in clinical development. A selective antagonist for PPARδ, GSK0660 has recently reported to exhibit inverse agonist activity and competes with agonist in cellular context [12]. QSAR studies are useful tools in the rational search for bioactive molecules. The main success of the QSAR method is the possibility to estimate the characteristics of new chemical compounds without the need to synthesize and test them. This analysis represents an attempt to relate structural descriptors of compounds with their physicochemical properties in the chemical, pharmaceutical and environmental spheres. This method includes data collection, molecular descriptor selection, correlation model development, finally model evaluation. QSAR studies have predictive ability and simultaneously provide deeper insight into mechanism of drug receptor interactions [13]. EXPERIMENTAL SECTION Data set In this QSAR study, biological and chemical data of 69 thiazoles (Figure 1) and 23 oxazoles (Figure 2) were used, which have been reported in the work of Epple et al.[10] (Table 1). In order to model and predict the biological effect of the specific compounds as functional agonists of PPARδ receptor, some physicochemical constants, molecular and topological descriptors were calculated using Chem3D ultra 10.0. Molecular Modeling The molecular structures of bisaryl substituted thiazoles and oxazoles were modeled using Chemdraw ultra 10.0 (Cambridge software), and then modeled structure is copied to Chem3D ultra 10.0 to create a 3D model and, finally subjected to energy minimization using molecular mechanics (MM2). The minimization was executed until the root mean square gradient value reached a value smaller than 0.001kcal/mol. Such energy minimized structures are considered for generating QSAR descriptors. Multiple linear regression (MLR) model development-variable selection The separation of the data into training and validation (test) sets was performed using random selection process. The complete MLR analysis was carried out using software Molegro Data Modeler v 2.0 (www.molegro.com) the values of descriptors selected for developing MLR model are presented in the Figures 3-5. QSAR models were generated using MLR based on manual selection method and were correlated to biological activity. PPARδ agonistic activity (- log EC50 µM) [10] was taken as the dependent variable. Leave-one-out (LOO) method is used to validate the results. 793

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4. A. Vasudeva Rao et al J. Chem. Pharm. Res., 2011, 3(2):792-801 ______________________________________________________________________________ Multiple Linear Regression (MLR) based best QSAR models of bisaryl substituted thiazoles (Equation 1) and oxazoles (Equation 2) for the prediction of PPARδ agonistic activity are shown as follows. QSAR Model-I (Equation-1) (-logEC50) = (-0.000531205 X (Molecular weight) - 0.00211013 X (Polar surface area) - 0.0105001 X (Andrews affinity) - 6.8519e-05 X (Wiener index) - 2.77791). QSAR Model-II (Equation-2) (-logEC50) = (0.000259889 X (Molecular Weight) + 9.54402e-05 X (Polar surface area) - 0.00233215 X (Andrews affinity) - 8.33044e-05 X (Wiener index) - 3.39367). Cross validation of QSAR models The test sets of bisaryl substituted thiazoles (Figure 3, 4) and oxazoles (Figure 5) were considered to evaluate the influence of descriptors molecular weight, wiener index, andrews affinity and polar surface area and their reliability on developed QSAR models. The predicted PPARδ agonistic activity obtained for validation set of thiazoles and oxazoles are shown in Figures 3,4. The experimental and predicted activities of thiazoles (Training and Test sets) and oxazoles (Training and Test sets) calculated using MLR models (Equations 1 & 2) indicating an excellent quality of correlation as shown in (Figure 6. A-H). 795

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7. A. Vasudeva Rao et al J. Chem. Pharm. Res., 2011, 3(2):792-801 ______________________________________________________________________________ RESULTS AND DISCUSSION The successful results of statistical analysis (Figure 5) led to the conclusion that activity of bisaryl substituted thiazoles and oxazoles as PPARδ agonists can be successfully modeled with molecular descriptors (molecular weight, wiener index, andrews affinity and polar surface area). Molecular weight is an important parameter that signifies the size of the molecule. Wiener index is a topological index of a molecule, defined as the sum of the numbers of edges in the shortest paths in a chemical graph between all pairs of non-hydrogen atoms in a molecule [14] related to molecular branching [15]. Andrews’s affinity defines the functional group contributions to drug-receptor interactions [16]. The polar surface area (PSA) is defined as the surface sum over all polar atoms, (usually oxygen and nitrogen), including also attached 798

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10. A. Vasudeva Rao et al J. Chem. Pharm. Res., 2011, 3(2):792-801 ______________________________________________________________________________ Acknowledgements One of the authors, Mr. A.VASUDEVARAO is grateful and thankful to Dr. Rene Thomsen, Molegro ApS, C.F. Moellers Alle8, Building 1110, DK-8000 Aarhus C, and Denmark for providing Molegro Data Modeller software licence for completion of this research work. REFERENCES [1]J Berger and DE Moller. Annu. Rev. Med.,2002, 3, 409. [2]L Serge; G Celine; H Dorte; LS Joaquin; JP Chantal; F Alexander and AG. Paul. Biochimica et Biophysica Acta.,2005, 313, 1740. [3]E Robert; R Ross; A Mihai; C Christopher; X Yongping; W Xing; W John; K Don; G Andrea; I Maya; S Enrique; HS Martin and ST Shin. Bioorg. Med. Chem. Lett., 2006, 4376, 16. [4]E Robert; A Mihai; R Ross; B Badry; ST Shinc; G Andrea and I Maya. Bioorg. Med. Chem. Lett., 2006, 16, 2969. [5]FF Gary; B Larry; MC Xue; E Noe; G Andrew; L Chitase; L Gina; P Jim; J S Roderick; P C Unangst and B K Trivedi. Bioorg. Med. Chem. Lett., 2007, 17, 3630. [6]GS Barry; SP Hari; NB Andrew; MW James; AW Deborah; HL Millard; XX Robert; ML Lisa; VM Raymond; H Stephane and MW Timothy. Bioorg. Med. Chem. Lett., 2008, 18, 5018. [7]F Hiroki; U Shinya; U Takayoshi; N Hidehiko; O Michitaka; M Makoto and M Naoki. Bioorg. Med. Chem. Lett., 2007, 17, 4351. [8]Z Rui; W Aihua; D Alan; P Patricia; X Jun; Z Peifang; Z Lubing; D Keith; V William and HK Gee. Bioorg. Med. Chem. Lett., 2007, 17, 3855. [9]ML Sznaidman; CD Haffner and PR Maloney. Bioorg. Med. Chem. Lett., 2003, 13, 1517. [10]E Robert; C Christopher; X Yongping; A Mihai; R Ross; W Xing; W John; SK Donald; T Tove; TBNT Van; CN Cara; H David; S Enrique; S Tracy; G Andrea; I Maya; S Martin and ST Shin. J. Med. Chem., 2010, 1, 77. [11]J Berger; MD Leibowitz and TW Doebber. J. Biol. Chem., 1999, 274, 6718. [12]BG Shearer; DJ Steger and J M Way. Molecular Endocrinology., 2008, 22, 523. [13]Sanja O Podunavac Kuzmanovic; Dragoljub D Cvetkovic and Dijana J Barna. Int. J. Mol. Sci., 2009, 10, 1670. [14] Bojan Mohar; Tomaz Pisanski. J. Math. Chemistry., 1988,2,267. [15] Ivan Gutman; T Kortvelyesi. Z Naturforsch., 1995, 50a,669. [16]PR Andrews; DJ Craik; JL Martin. J .Med. Chem., 1984, 27, 1648. [17]P Ertl; B Rohde and P Selzer. J. Med. Chem., 2000, 43, 3714. 801