QSAR STUDIES ON HUMAN CARBONIC ANHYDRASE II INHIBITORS

Carbonic anhydrase II is one of the forms of human α carbonic anhydrases which are ubiquitous metalloenzymes that catalyze inter-conversion of carbon dioxide and water to bicarbonate and proton, overexpression of which leads to disorders such as glaucoma. 2D and 3D Quantitative Structure Activity Relationship studies were carried out on previously synthesized series of sulfanilamide derivatives by VLife MDS software using stepwise variable, multi-linear regression and k-nearest neighbor molecular field analysis methods. 2D-QSAR model depicts contribution of halogens (such as chlorine and fluorine), methylene and oxygen atoms to inhibition of human carbonic anhydrases II activity. Using k-nearest neighbor molecular field analysis method two 3D-QSAR models (model A and B) were generated from which model A was found to be the best validated model with q2 (0.9494), pred_r2 (0.7367) and q2 _ se (0.2037). It displayed the fact that the inhibitory action of sulfanilamide derivatives against human carbonic anhydrases II is influenced by hydrophobicity and electro positivity.

QSAR Studies of Sulfonamide Hydroxamates Derivatives as MMP-2 Inhibitors Topomer CoMFA and Molecular Docking

Background: In recent years, cancer has become the main cause of death and it is a serious threat to human health, so the development of new, selective and safe anticancer drugs is still the focus of medical research. Matrix metalloproteinases-2 (MMP-2) has been determined to play an important role in the regulation of tumor angiogenesis, which is closely related to the development of the tumor. Therefore, MMP-2 is considered as a promising target for tumor therapy. In this study, Tomper comparative molecular field analysis (Topomer CoMFA) and molecular docking were used to investigate the important role of sulfonamide hydroxamate derivatives, an inhibitor of MMP-2, in the inhibition of angiogenesis. Methods: Quantitative structure active relationship (QSAR) models of 35 sulfonamide hydroxamate derivatives with inhibitory MMPs were developed. The quantitative structure-activity relationship (QSAR) model was built by using Topomer comparative molecular field analysis (Topomer CoMFA) technique. Results and Conclusion: The results show that the cross-validated q2 value of the Topomer CoMFA model is 0.881 and the non-cross-validated r2 value is 0.967. The results show that the model is reasonable and reliable, and has good prediction ability. Molecular docking studies were used to find the actual conformations of chemicals in active sites of cancer protease, as well as the binding mode pattern to the binding site in MMP-2. The information provided by the 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 35 sulfonamide hydroxamate derivatives and help to design potential anti-cancer protease inhibitor molecules. Conclusion: Thirty-five analogs were used in the 3D-QSAR study. Topomer CoMFA 3D-QSAR method was used to build the model, and the model was well predicted and statistically validated. The results of 3D-QSAR and molecular docking analysis provide theoretical guidance for the synthesis of new MMP-2 inhibitors.

In Silico ADME and QSAR Studies on a Set of Coumarin Derivatives As Acetylcholinesterase Inhibitors Against Alzheimer’s Disease: CoMFA, CoMSIA, Topomer CoMFA, and HQSAR

Background: Alzheimer’s disease (AD) is increasingly being recognized as one of the lethal diseases in older people. Acetylcholinesterase (AChE) has proven to be the most promising target in AD, used for designing drugs against AD. Methods: In silico studies, 2D- or 3D-QSAR like hologram QSAR (HQSAR), Topomer comparative molecular field analysis (Topomer CoMFA), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to generate QSAR models for acetylcholinesterase inhibitors. Results: Acetylcholinesterase inhibitors used for the present study contain a series of 7- hydroxycoumarin derivatives connected by piperidine, piperazine, tacrine, triazole, or benzyl fragments through alkyl or amide spacer training set compounds were used to generate best model with a HQSAR q2 value of 0.916 and r2 value of 0.940; a Topomer CoMFA q2 value of 0.907 and r2 value of 0.959, CoMFA q2 value of 0.880 and r2 value of 0.960; and a CoMSIA q2 value of 0.865 and r2 value of 0.941. In addition, contour plots obtained from QSAR models suggested the significant regions that influenced the AChE inhibitory activity. Conclusion: In light of these results, this study provides knowledge about the structural requirements for the development of more active acetylcholinesterase inhibitors. In addition, the predicted ADME profile helps us to find CNS active molecules, the obtained prediction compared with well-known AChE inhibitors viz., ensaculin, tacrine, galantamine, rivastigmine, and donepezil. Based on the knowledge obtained from these studies, the hybridization approach is one of the best ways to find lead compounds and these findings can be useful in the treatment of Alzheimer's disease.