DESIGN AND DEVELOPMENT OF PPARDELTA SELECTIVE LIGANDS USING STRUCTURE BASED AND LIGAND BASED COMPUTATIONAL APPROACHES

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Comparative QSAR analyses of competitive CYP2C9 inhibitors using three-dimensional molecular descriptors

Publication . Lather, Viney; Fernandes, Miguel X.

One of the biggest challenges in QSAR studies using three-dimensional descriptors is to generate the bioactive conformation of the molecules. Com parative QSAR analyses have been performed on a dataset of 34 structurally diverse and competitive CYP2C9 inhibitors by generating their lowest energy conformers as well as additional multiple conformers for the calculation of molecular de scriptors. Three-dimensional descriptors account ing for the spatial characteristics of the molecules calculated using E-Dragon were used as the inde pendent variables. The robustness and the predic tive performance of the developed models were verified using both the internal [leave-one-out (LOO)] and external statistical validation (test set of 12 inhibitors). The best models (MLR using GET AWAY descriptors and partial least squares using 3D-MoRSE) were obtained by using the multiple conformers for the calculation of descriptors and were selected based upon the higher external pre diction (R2 test values of 0.65 and 0.63, respectively) and lower root mean square error of prediction (0.48 and 0.48, respectively). The predictive ability of the best model, i.e., MLR using GETAWAY de scriptors was additionally verified on an external test set of quinoline-4-carboxamide analogs and resulted in an R2 test value of 0.6. These simple and alignment-independent QSAR models offer the possibility to predict CYP2C9 inhibitory activity of chemically diverse ligands in the absence of X-ray crystallographic information of target protein structure and can provide useful insights about the ADMET properties of candidate molecules in the early phases of drug discovery.

2011Journal article

Open access

QSAR models for prediction of PPARδ agonistic activity of indanylacetic acid derivatives

Publication . Lather, Viney; Fernandes, Miguel X.

Peroxisome Proliferator Activated Receptor b/d (PPAR b/d), one of three PPAR isoforms is a member of nuclear receptor superfamily and ubiquitously expressed in several metabolically active tissues such as liver, muscle, and fat. Tissue specific expression and knock-out studies suggest a role of PPARd in obesity and metabolic syndrome. Specific and selective PPARd ligands may play an important role in the treatment of metabolic disorders. Indanylacetic acid derivatives reported as potent and specific ligands against PPARd have been studied for the Quantitative Structure – Activity Relationships (QSAR). Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. Four different approaches, i.e., random selection, hierarchical clustering, k-means clustering, and sphere exclusion method were used to classify the dataset into training and test subsets. Forward stepwise Multiple Linear Regression (MLR) approach was used to linearly select the subset of descriptors and establish the linear relationship with PPARd agonistic activity of the molecules. The models were validated internally by Leave One Out (LOO) and externally for the prediction of test sets. The best subset of descriptors was then fed to the Artificial Neural Networks (ANN) to develop non-linear models. Statistically significant MLR models; with R2 varying from 0.80 to 0.87 were generated based on the different training and test set selection methods. Training of ANNs with different architectures for the different training and test selection methods resulted in models with R2 values varying from 0.83 to 0.94, which indicates the high predictive ability of the models.

2009Journal article

Open access

Quantitative structure-activity relationship models with receptor-dependent descriptors for predicting peroxisome proliferator-activated receptor activities of thiazolidinedione and oxazolidinedione derivatives

Publication . Lather, Viney; Kairys, Visvaldas; Fernandes, Miguel X.

A quantitative structure–activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor a and the peroxisome proliferator activated receptor c agonistic activities of thiazo lidinedione and oxazolidinedione derivatives and quantitative descriptors, Vsite calculated in a receptor-dependent manner is modeled. These descriptors quantify the volume occupied by the optimized ligands in regions that are either com mon or specific to the superimposed binding sites of the targets under consideration. The quantita tive structure–activity relationship models were built by forward stepwise linear regression model ing for a training set of 27 compounds and vali dated for a test set of seven compounds, resulting in a squared correlation coefficient value of 0.90 for peroxisome proliferator-activated receptor a and of 0.89 for peroxisome proliferator-activated receptor c. The leave-one-out cross-validation and test set predictability squared correlation coeffi cient values for these models were 0.85 and 0.62 for peroxisome proliferator-activated receptor a and 0.89 and 0.50 for peroxisome proliferator-acti vated receptor c respectively. A dual peroxisome proliferator-activated receptor model has also been developed, and it indicates the structural features required for the design of ligands with dual peroxisome proliferator-activated receptor activity. These quantitative structure–activity relationship models show the importance of the descriptors here introduced in the prediction and interpretation of the compounds affinity and selectivity.

2009Journal article

Open access

Toward the design of mutation‐resistant enzyme inhibitors: further evaluation of the substrate envelope hypothesis

Publication . Kairys, Visvaldas; Gilson, Michael K.; Lather, Viney; Schiffer, Celia A.; Fernandes, Miguel X.

Previous studies have shown the usefulness of the substrate envelope concept in the analysis and prediction of drug resistance profiles for human immunodeficiency virus protease mutants. This study tests its applicability to several other thera peutic targets: Abl kinase, chitinase, thymidylate synthase, dihydrofolate reductase, and neuramini dase. For the targets where many (‡6) mutation data are available to compute the average mutation sen sitivity of inhibitors, the total volume of an inhibitor molecule that projects outside the substrate enve lope Vout, is found to correlate with average muta tion sensitivity. Analysis of a locally computed volume suggests that the same correlation would hold for the other targets, if more extensive muta tion data sets were available. It is concluded that the substrate envelope concept offers a promising and easily implemented computational tool for the design of drugs that will tend to resist muta tions. Software implementing these calculations is provided with the ’Supporting Information’.

2009Journal article

Open access