Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations

Anticancer potential and through study of the cytotoxicity mechanism of ionic liquids that are based on the trifluoromethanesulfonate and bis(trifluoromethylsulfonyl)imide anions

Ionic liquids (ILs) are known for their unique physicochemical properties. However, despite the great number of published papers, still little attention has been paid to their biological activity. Anticancer potential and the molecular mechanisms underlying the toxicity of these compounds are especially interesting and still unexplored. In the current work, a broad analysis of the cytotoxicity towards colon and breast cancers as well as glioblastoma of the ILs with pyridinium, piperidinium, pyrrolidinium, and imidazolium cations and trifluoromethanesulfonate or bis(trifluoromethylsulfonyl)imide anions indicated previously as the most toxic for normal human dermal fibroblasts were presented. In the case of MCF-7 cells, the activity of 1-decyl-3-methylimidazolium trifluoromethanesulfonate was more than twice as high as cisplatin. It was found that the inhibition of the cell cycle of colon cancer and glioblastoma cells occurs in different phases. More importantly, the different types of cell death were detected for both selected ILs, namely 1-hexyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide and 1-hexyl-3-methylimidazolium trifluoromethane-sulfonate, on colon cancer and glioblastoma, respectively, apoptosis and autophagy, confirmed at the gene and protein levels. Additionally, kinetic studies of the reactive oxygen species indicated that the tested ILs disturbed the cellular redox homeostasis.

Inhibition activity prediction for a dataset of candidates' drug by combining fuzzy logic with MLR/ANN QSAR models

A hybrid of artificial intelligence simple and low computational cost QSAR was used. Approximately 90 pyridinylimidazole-based drug candidates with a range of potencies against p38R MAP kinase were investigated. To obtain more flexibility and effective capability of handling and processing information about the real world, in this case, the fuzzy set theory was introduced into the QSAR. An integration of multiple linear regression and artificial neural network with adaptive neuro-fuzzy inference systems (ANFIS) was developed to predict the inhibition activity. The algorithm of ANFIS was applied to identify the suitable variables and then to find the optimal descriptors. The gradient descent with momentum backpropagation ANN was used to establish the nonlinear multivariate relationships between the chemical structural parameters and biological response. A comparison between the result of the proposed linear and nonlinear regression showed the superiority of QSAR modeling by ANFIS-ANN method over the MLR. The results demonstrated that the ANFIS could be applied successfully as a feature selection. The appearance of Diam, Homo, and LogP descriptors in the model showed the importance of the steric, electronic, and thermodynamic interactions between a drug and its target site in the distribution of a compound within a biosystem and its interaction with competing for binding sites.

Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches

In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R²(train) = 0.935, R²(test) = 0.902, Q²(LOO) = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC(-)), oprea's lead-like (opr_leadlike), subdivided van der Waal's surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R²(train) = 0.944, R²(test) = 0.892, Q²(LOO) = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC50 values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity.