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Mukesh Chandra Sharma et al /Int.J. PharmTech Res.2010,2(2)


(HDL) cholesterol.6-7 The role of PPAR γ has been extensively studied and is known to be involved with glucose homeostasis, insulin sensitization, and fat storage. PPAR γ agonists, such as rosiglitazone, 5-{4- [2- (methyl-pyridin-2-yl-amino)-ethoxy]-benzyl}- thiazolidine- 2,4-dione, increase insulin sensitivity and have been approvedfor the treatment of type 2 diabetes.8 While not as extensively studied as the other subtypes, the role of PPAR ß has become clearer recently with the generation of potent, selective ligands for this PPAR subtype. As exemplified in studies with GW501516,{2-methyl- 4-[4-methyl-2-(4-trifluoromethyl-phenyl)-thiazol ylmethylsulfanyl]- phenoxy}-acetic acid, PPAR γ/δ activation appears to increase fatty acid b-oxidation, insulin sensitivity, and HDL cholesterol. 9-10

MATERIAL AND METHODS A dataset consisting of a series of 3-{4-[3-(2-aryl- phenoxy) butoxy]-phenyl} propionic acids derivatives acting PPAR γ/δ dual activators (Table 1) has been chosen to develop a dual-response QSAR model. The biological activities of the molecules have been expressed as the binding affinities measured as IC50 values in micromolar using recombinant PPAR γ/δ

by5. For QSAR analysis, these have been converted to pIC50 (log _ IC50) values in molar terms (Table 1). Various 2D descriptors (a total of 208) like element counts, molecular weight, molecular refractivity, log P, topological index, electro-topological index, Baumann alignment independent topological descriptors, etc., were calculated using VLifeMDS software 11. The preprocessing of the independent variables (i.e., descriptors) was done by removing invariable (constant column) and cross-correlated descriptors (with r>0.99) which resulted in total 132 descriptors to be used for QSAR analysis. All the twenty eight compounds were built on workspace of molecular modeling software V-Life MDS 3.5, which is a







compounds were then subjected to conformational analysis and energy minimization using montocarlo conformational search with RMS gradient of 0.001 kcal/mol and iteration limit of 10000 using a MMFF94 force field. Montocarlo conformational search method is similar to the RIPS method that generates a new molecular conformation by randomly perturbing the position of each coordinate of each atom in molecule, followed by energy minimization and optimization is necessary process for proper alignment of molecules around template. Most stable structure for each compound was generated after energy minimization and used for calculating various physico-chemical descriptors like thermodynamic, steric and electronic. The various descriptors selected for 2D QSAR were vdWSurfaceArea (van der Waals surface area of the

molecule), –vePotential Surface Area (total van der Waals surface area with negative electrostatic potential of the molecule), +vePotentialSurfaceArea (total van der Waals surface area with positive electrostatic potential of the molecule) dipole moment, YcompDipole (y component of the dipole moment), element count, slogP, path count, cluster, distance

based topological indices, hydrophobic and hydrophilic

connectivity index, areas like SA Most

Hydrophilic (Most hydrophilic value on the vdW surface by Audry Method using Slogp), SAMostHydrophobic Hydrophilic Distance (distance between most hydrophobic and hydrophilic point on the vdW surface by Audry Method using Slogp), SAHydrophilicArea (vdW surface descriptor showing hydrophilic surface area by Audry Method using SlogP) and SKMostHydrophilic (Most hydrophilic value on the vdW surface by Kellog Method using Slogp), radius of gyration, Wiener’s index, moment of i n e r t i a , s e m i - e m p i r i c a l d e s c r i p t o r s , H O M O ( H i g h e s occupied t molecular orbital), (lowest LUMO

unoccupied molecular orbital), heat of formation and ionization potential. Besides these all alignment independent descriptors were also calculated. The hydrophobic descriptors govern the movement of a drug molecule across the biological membranes in order to interact with the receptor by vander Waals binding forces whereas both electronic and steric descriptors influence the affinity of a drug molecule necessary for proper drug- receptor interaction. The optimal training and test sets were generated by either random selection method or the sphere exclusion algorithm. A commonly used ratio of training to validation objects (test set), which was also adopted in this work, is 70%: 30% 12. However, rational splitting was accomplished by applying a sphere-exclusion type algorithm 13-17. In classical sphere-exclusion algorithm the molecules are selected whose similarities with each of the other selected molecules are not higher than a defined threshold. Each selected molecule generates a hyper-sphere around itself, so that any molecule inside the sphere is excluded from the selection in the train set and driven toward the test set. The number of compounds selected and the diversity among them can be determined by adjusting the radius of the sphere (R). Statistical analysis Models were generated by using three significant statistical methods, namely, partial least square






component analysis. The cross-validation analysis was performed using the leave-one-out method. In the selected equations, the cross-correlation limit was set at 0.5, the number of variables at 10, and the term selection criteria at r2. An F value was specified to evaluate the significance of a variable. The higher the

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