Juan C. Garro Martinez

Dihydrofolate reductase inhibitors: a quantitative structure–activity relationship study using 2D-QSAR and 3D-QSAR methods

In this work, we study the structure–activity relationship of a series of Dihydrofolate reductase inhibitors by two-dimensional quantitative activity–structure relationship and three-dimensional quantitative activity–structure relationship techniques. The two-dimensional quantitative activity–structure relationship models were developed by using two different types of topological molecular descriptors, PaDEL and Dragon descriptors. The models showed an excellent predictive power, R2train = 0.916 and R2val = 0.806 for the PaDEL, and R2train = 0.952 and R2val = 0.963 for those obtained with Dragon descriptors. Simple molecular descriptors as maxHCsats, IC3, SPI, SIC2, and GATS5p were adequate to obtain predictive models. The three-dimensional quantitative activity–structure relationship was performed through three variable selected approaches, Partial Linear Square (PLS), Fractional Factorial Design (FFD) and Uninformative Variable Elimination-Partial Linear Square (UVE-PLS) using the Open3DQSAR software. All the 2D and 3D models were validated using two compounds (number 24 and 25), which were synthesized and presented here for the first time. Their biological activities were correctly predicted by all the quantitative activity–structure relationship models. Finally, we proposed three compounds (26, 27, and 28), which showed a high predicted Dihydrofolate reductase inhibitory activity. Molecular docking study suggested that compounds bind to receptor similarly to the most active inhibitors.

QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity

Introduction: Quantitative structure–activity relationships (QSAR and 3D-QSAR) have been applied in the last decade to obtain a reliable statistical model for the prediction of the anticonvulsant activities of new chemical entities. However, despite the large amount of information on QSAR, no recent review has published and discussed this data in detail. Areas covered: In this review, the authors provide a detailed discussion of QSAR studies that have been applied to compounds with anticonvulsant activity published between the years 2003 and 2013. They also evaluate the mathematical approaches and the main software used to develop the QSAR and 3D-QSAR model. Expert opinion: QSAR methodologies continue to attract the attention of researchers and provide valuable information for the development of new potentially active compounds including those with anticonvulsant activity. This has been helped in part by improvements in the size and performance of computers; the development of specific software and the development of novel molecular descriptors, which have given rise to new and more predictive QSAR models. The extensive development of descriptors, and the way by which descriptor values are derived, have allowed the evolution of the QSAR methods. This evolution could strengthen the QSAR methods as an important tool in research and development of new and more potent anticonvulsant agents.

In Silico Modeling of the Molecular Structure and Binding of Leukotriene A4 into Leukotriene A4 Hydrolase

A combined molecular docking and molecular structure in silico analysis on the substrate and product of leukotriene A4 hydrolase (LTA4H) was performed. The molecular structures of the substrate leukotriene A4 (LTA4) and product leukotirene B4 (LTB4) were studied through density functional theory (DFT) calculations at the B3LYP/6‐31 + G(d) level of theory in both gas and condensed phases. The whole LTB4 molecule was divided into three fragments (hydrophobic tail, triene motif, and a polar acidic group) that were subjected to a full conformational study employing the most stable conformations of them to build conformers of the complete molecule and geometry optimize further. LTA4 conformers’ structures were modeled from the LTB4 minimum energy conformers. Both protonated and deprotonated species of LTA4 and LTB4 were analyzed according to pKa values found in the literature. Finally, a binding model of LTA4 with LTA4 hydrolase is proposed according to docking results that show intermolecular interactions that position the protonated and deprotonated ligand in the active site, in excellent agreement with the model suggested from LTA4H‐inhibitors crystallographic data.

Synthesis, anti-parasitic activity and QSAR study of a new library of polysubstituted tetrahydronaphtho[1,2-b]azepines

A new series of twenty two 2-exo-aryl(heteroaryl)-1,4-epoxytetrahydronaphtho[1,2-b]azepines 8–10 and eighteen cis-2-aryl(heteroaryl)-4-hydroxytetrahydronaphtho[1,2-b]azepines 11–13 were synthesized, and most of them were tested for their ability to inhibit the in vitro growth of the extracellular forms of Trypanosoma cruzi and Leishmania infantum parasites. Cell toxicity was also determined on Vero and THP-1 mammalian cells. Seventeen compounds exhibited potent activity against the epimastigotes (IC50 lower than 20 µM), without cytotoxicity on Vero cells. Ten compounds also showed remarkable anti-leishmanial properties against the promastigote form of the parasite (IC50 lower than 20 µM), but most of them were found cytotoxic for HTP-1 cells. We have also performed a quantitative structure activity relationship analysis by means of the multivariate lineal regression (MLR) technique with a family of ninety-four tetrahydro-1-benzazepine and tetrahydronaphtho[1,2-b]azepine derivatives with anti-parasitic activity. The aim of this study is to develop a tool that permits us to elucidate the structural features, which influence in the bioactivity of these compounds. The QSAR prediction models for Trypanosoma cruzi and Leishmania infantum were acceptable with a correlation coefficient values (R) of 0.668 and 0.852, respectively, in the prediction of those activities.