Smita Sharma

Molecular Modeling Studies of Substituted 2,4,5-Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1 Receptor Antagonists

The development of new therapies to treat hypertension and cardiovascular diseases. A series of 2,4,5-trisubstituted triazolinones aryl and nonaryl derivatives were subjected to Group-based QSAR, -nearest neighbour molecular field analysis, and pharmacophore mapping. Multiple linear regression (MLR) methodology coupled with feature selection method namely simulated annealing, was applied to derive Group based QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The best physicochemical descriptors, namely, R1chiV1, R2T_N_O_3, R2chlorines count, R2T_C_N_4, and R2SssNHE index, contribute significantly to the biological activity. The statistically significant best Group-based QSAR model has and with pred_. The 3D-QSAR studies were performed using the simulated annealing selection -nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient and predicate activity pred_ were obtained. Contour maps using this approach showed that steric, electrostatic, and hydrophobic effects dominantly determine binding affinities. Pharmacophore hypotheses were generated by the mol sign module and found to contain common features like hydrogen bond donor acceptor, donor, positive, negative ionizable, and hydrophobic features. This model can be used for preliminary screening of large number of substituted 3H-1,-2,-4 triazolinone aryl and nonaryl derivatives. The information rendered by 3D-QSAR models may lead to a better understanding of structural requirements of triazolinone aryl and nonaryl derivatives and also aid in designing novel potent antihypertensive molecules.

Epidemiological trends of pediatric trauma: A single-center study of 791 patients

Aim: To assess the various epidemiological parameters that influence the causation of trauma as well as the consequent morbidity and mortality in the pediatric age group. Materials and Methods: A prospective study of 791 patients of less than 12 years age, was carried out over a period of 1 year (August 2009 to July 2010), and pediatric trauma trends, with regards to the following parameters were assessed: Age group, sex, mode of trauma, type of injury, place where the trauma occurred and the overall mortality as well as mortality. Results: Overall trauma was most common in the school-going age group (6-12 years), with male children outnumbering females in the ratio of 1.9:1. It was observed that orthopedic injuries were the most frequent (37.8%) type of injuries, whereas fall from height (39.4%), road traffic accident (27.8%) and burns (15.2%) were the next most common modes of trauma. Home was found out to be the place where maximum trauma occurred (51.8%). Maximum injuries happened unintentionally (98.4%). Overall mortality was found out to be 6.4% (n = 51). Conclusions: By knowing the epidemiology of pediatric trauma, we conclude that majority of pediatric injuries are preventable and pediatric epidemiological trends differ from those in adults. Therefore, preventive strategies should be made in pediatric patients on the basis of these epidemiological trends.

Prediction of potent therapeutic anticonvulsant ligands to N-methyl-d-aspartate receptors among substituted 4,6-dichloroindole-2-carboxylic acids: Molecular modeling approach

Abstract We performed two- and three-dimensional quantitative structure–activity relation (QSAR) pharmacophore studies with a series of substituted 4,6-dichloroindole-2-carboxylic acid analogs in order to elucidate the structural properties required for high affinity for the glycine binding site of the N-methyl-d-aspartate receptor. The partial least-squares method coupled with various feature selection methods (stepwise, genetic algorithm and simulated annealing) were used to derive QSAR models, which were validated for statistical significance and predictive ability by internal and external validation. The best two-dimensional QSAR model was selected, which had a correlation coefficient, r2 = 0.8577, a cross-validated squared correlation coefficient, q2 = 0.7118 and an external predictive ability pred_r2 = 0.7642. Molecular field analysis was used to construct the best three-dimensional QSAR model by the genetic algorithm–partial least-squares method, which showed good correlational and predictive capability: q2 = 0.7538, q2_se = 0.4833 and pred_r2 = 0.7019. In a series of 4,6-dichloroindole-2-carboxylic acid derivatives, the pharmacophore model based on the chemical feature with the lowest root mean square deviation (2.187 nm) consisted of one aromatic feature, one hydrogen bond donor, one hydrogen bond acceptor, one aliphatic and one positive coefficient feature. A representative set of 4,6-dichloroindole-2-carboxylic acid compounds with effective biological activity and a good glycine binding site on the N-methyl-d-aspartate receptor were identified, which may be potential leads for drugs with anticonvulsant activity.

QSAR studies on pyrazole-4-carboxamide derivatives as Aurora A kinase inhibitors

Abstract Cancer is a cellular disease characterized by the progressive, persistent, abnormal, purposeless, and uncontrolled proliferation of tissues. 2D-QSAR was performed on a series of N,1,3-triphenyl-1H-pyrazole-4-carboxamides as anticancer agents to gain insights into the structural determinants and their structure–activity relationship. The 2D-QSAR model using 20 molecules in the training set gave an r2 value of 0.8059 and a pred_r2 value of 0.7861. In models, it is shown that bulky electron-withdrawing substituents at the R1 and R2 position of the chain are favoured for inhibitory activity. Hence the model proposed in this work provides important structural insights into designing novel pyrazole-4-carboxamide derivatives with specific Aurora A kinase inhibition.

Molecular Modeling Studies of Thiophenyl C-Aryl Glucoside SGLT2 Inhibitors as Potential Antidiabetic Agents

A QSAR study on thiophenyl derivatives as SGLT2 inhibitors as potential antidiabetic agents was performed with thirty-three compounds. Comparison of the obtained results indicated the superiority of the genetic algorithm over the simulated annealing and stepwise forward-backward variable method for feature selection. The best 2D QSAR model showed satisfactory statistical parameters for the data set (r 2 = 0.8499, q 2 = 0.8267, and pred_r 2 = 0.7729) with four descriptors describing the nature of substituent groups and the environment of the substitution site. Evaluation of the model implied that electron-rich substitution position improves the inhibitory activity. The good predictive 3D-QSAR models by k-nearest neighbor (kNN) method for molecular field analysis (MFA) have cross-validated coefficient q 2 value of 0.7663 and predicted r 2 value of 0.7386. The results have showed that thiophenyl groups are necessary for activity and halogen, bulky, and less bulky groups in thiophenyl nucleus enhanced the biological activity. These studies are promising for the development of novel SGLT2 inhibitor, which may have potent antidiabetic activity.

Molecular modeling studies of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives as phosphatase SerB653 inhibitors

To understand the quantitative structure–activity relationships properties of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives, and to design inhibitors of phosphatase SerB653 were developed. The statistical parameters of two-dimensional quantitative structure–activity relationship model showed it has good reliability and predictive ability (q2 = 0.7180, F test = 62.046, pred_r2 = 0.7519). The best two-dimensional model suggests a chlorine atom substitution at position X4 and Y1 for enhance activity. Three-dimensional quantitative structure–activity relationship was carried out using k-nearest neighbor method and showed cross-validated correlation coefficient (q2) of 0.7484, and a predicted r2 for the external test (pred_r2) of 0.6895 were obtained with best three-dimensional quantitative structure–activity relationship model. The influences of steric and electrostatic field effects generated by the contribution plot are analyzed. Pharmacophore approach for SerB653 inhibitor consists of hydrogen bond acceptor, hydrogen bond donor, and aromatic region. Two-dimensional quantitative structure–activity relationship and three-dimensional quantitative structure–activity relationship, pharmacophore analyses of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives can provide more useful information and important structural insights for designing potent phosphatase SerB653 inhibitors.