Molecular modeling studies,synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors

In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q² and a non-cross-validation r², which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC₅₀) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.     

Molecular docking and 3D-QSAR on 2-(oxalylamino) benzoic acid and its analogues as protein tyrosine phosphatase 1B inhibitors

In this paper, molecular docking technique was used to investigate the binding conformation of twelve 2-(oxalylamino) benzoic acid (OBA) inhibitors in the active site of PTP1B. The predicted binding affinities are linearly correlated to the experimental values (r² = 0.859). Furthermore, comparative molecular field analysis (CoMFA) was conducted based on the binding conformation predicted by molecular docking. The predicted CoMFA model has satisfactory statistical significance and good actual predicted power. The information from molecular docking and CoMFA may give us some valuable hints to the optimization of lead compounds.     

3D–QSAR studies of orvinol analogs as κ-opioid agonists

Orvinols are potent analgesics that target opioid receptors. However, their analgesic mechanism remains unclear and no significant preference for subtype opioid receptor has been achieved. In order to find new orvinols that target the κ-receptor, comparative 3D–QSAR studies were performed on 26 orvinol analogs using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best predictions for the κ-receptor were obtained with the CoMFA standard model (q ²=0.686, r ²=0.947) and CoMSIA model combined steric, electrostatic, hydrophobic, and hydrogen bond donor/acceptor fields (q ²=0.678, r ²=0.914). The models built were further validated by a test set made up of seven compounds, leading to predictive r ² values of 0.672 for CoMFA and 0.593 for CoMSIA. The study could be helpful for designing and prepare new category κ-agonists from orvinols.      

CoMFA and CoMSIA analysis of 2,4-thiazolidinediones derivatives as aldose reductase inhibitors

Diabetes remains a life-threatening disease. The clinical profile of diabetic subjects is often worsened by the presence of several long-term complications, for example neuropathy, nephropathy, retinopathy, and cataract. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 2,4-thiazolidinediones derivatives as aldose reductase (ALR2) inhibitors. Molecular ligand superimposition on a template structure was finished by the database alignment method. The 3D-QSAR models resulted from 44 molecules gave q ² values of 0.773 and 0.817, r ² values of 0.981 and 0.979 for CoMFA and CoMSIA, respectively. The contour maps from the models indicated that a large volume group next to the R-substituent will increase the ALR2 inhibitory activity. In fact, adding a -CH₂COOH substituent at the R-position would generate a new compound with higher predicted activity.     

Particular interaction between pyrimethamine derivatives and quadruple mutant type dihydrofolate reductase of Plasmodium falciparum: CoMFA and quantum chemical calculations studies

Comparative molecular field analysis (CoMFA) was performed on twenty-three pyrimethamine (pyr) derivatives active against quadruple mutant type (Asn51Ile, Cys59Arg, Ser108Asn, Ile164Leu) dihydrofolate reductase of Plasmodium falcipaarum (PfDHFR). The represented CoMFA models were evaluated based on the various three different probe atoms, C_sp3 (+1), O_sp3 ( ? 1) and H (+1), resulting in the best model with combined three types of probe atoms. The statistical results were = 0.702, S_press = 0.608, = 0.980, s = 0.156, and = 0.698 which can explain steric contribution of about 50%. In addition, an understanding of particular interaction energy between inhibitor and surrounding residues in the binding pocket was performed by using MP2/6-31G(d,p) quantum chemical calculations. The obtained results clearly demonstrate that Asn108 is the cause of pyr resistance with the highest repulsive interaction energy. Therefore, CoMFA and particular interaction energy analyses can be useful for identifying the structural features of potent pyr derivatives active against quadruple mutant type PfDHFR.     

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC₅₀ values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q²_LOO, Q²_(F2), and Q²_(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.     

Key Features for Designing Phosphodiesterase-5 Inhibitors

Abstract

Phosphodiesterase superfamily is the key regulator of 3′,5′-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q², and non cross-validated coefficient, r², values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate.

Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

CoMFA studies and in vitro evaluation of some 3-substituted benzylthio quinolinium salts as anticryptococcal agents

The 3-dimensional quantitative structure–activity relationship (3D-QSAR) molecular modeling technique or comparative molecular field analysis (CoMFA) has been used to design analogs of the natural product cryptolepine (1). Twenty-three compounds with their in vitro biological activities (IC₅₀ values) against Crytococcus neoformans were used to generate the training set database of compounds for the CoMFA studies. The cross-validated q², noncross-validated r², and partial least squares (PLS) analysis results were used to predict the biological activity of 11 newly designed test set compounds. The best CoMFA model produced a q² of 0.815 and an r² of 0.976 indicating high statistical significance as a predictive model. The steric and electrostatic contributions from the contour map were interpreted from the color-coded contour plots generated from the PLS model and the active structural components for potency against C. neoformans were determined and validated in the test set compounds. The 3-substituted benzylthio quinolinium salts (4) that make up the test set were synthesized and evaluated based on the predicted activity from the CoMFA model and the results produced a good correlation between the predicted and experimental activity (R = 0.82). Thus, CoMFA has served as an effective tool to aid the design of new analogs and in this case, it has aided the identification of compounds equipotent with amphotericin B, the gold standard in antifungal drug design.     

Benzimidazole-based derivatives as privileged scaffold developed for the treatment of the RSV infection: a computational study exploring the potency and cytotoxicity profiles

Respiratory syncytial virus (RSV) has been identified as a main cause of hospitalisation in infants and children. To date, the current therapeutic arsenal is limited to ribavirin and palivizumab with variable efficacy. In this work, starting from a number of in-house series of previously described anti-RSV agents based on the benzimidazole scaffold, with the aim at gaining a better understanding of the related chemical features involved in potency and safety profiles, we applied a computational study including two focussed comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The results allowed us to derive useful suggestions for the design of derivatives and also to set up statistical models predicting the potency and selectivity index (SI?=?CC₅₀/EC₅₀) of any new analogue prior to synthesis. Accordingly, here, we discuss preliminary results obtained through the applied exhaustive QSAR analyses, leading to design and synthesise more effective anti-RSV agents.     

Exploring the binding features of rimonabant analogues and acyclic CB<Subscript>1</Subscript> antagonists: docking studies and QSAR analysis

In order to elucidate the structural requirements for human CB₁ receptor antagonism, 78 antagonists belonging to five different chemical classes were selected from the literature and docked into the receptor binding site, built by homology modeling techniques. To further explore the structure-activity relationships within the considered chemical classes, a pharmacophore model and a QSAR analysis were developed. In a first step five alignments, one for each group of compounds were generated. All of them were then submitted to a MOE pharmacophore search in order to obtain a final pharmacophore model representative of the whole dataset which was used to elaborate the following 3D-QSAR analysis, by means of the CoMFA methodology. The results of these investigations are expected to be useful in the process of design and development of new potent CB₁ antagonists. Figure Compounds 1-78 are aligned into the putative CB1 receptor binding site. The three key features shared by all of them are reported in coloured spheres. The hydrophobic/aromatic ones are depicted in purple while the acceptor functions are coloured in blue.