Pharmacophore Refinement and 3D-QSAR Studies of Histamine H3 Antagonists

An Integrated in silico Approach and in vitro Study for the Discovery of Small-Molecule USP7 Inhibitors as Potential Cancer Therapies

The ubiquitin-specific protease 7 (USP7) is a highly promising well-validated target for a variety of malignancies. USP7 is critical in regulating the tumor suppressor p53 along with numerous epigenetic modifiers and transcription factors. Previous studies showed that USP7 inhibitors led to increased levels of p53 and anti-proliferative effects in hematological and solid tumor cell lines. Thus, this study aimed to identify potent and safe USP7 hit inhibitors as potential anti-cancer therapeutics via an integrated computational approach that combines pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations and post-MD free energy calculations. In this study, the crystal structure of USP7 has been extensively investigated using a combination of three different chemical pharmacophore modeling approaches. We then screened ∼220.000 drug-like small molecule library and the hit ligands predicted to be nontoxic were evaluated further. The identified hits from each pharmacophore modeling study were further examined by 1-ns short MD simulations and MM/GBSA free energy analysis. In total, we ran 1 ns MD simulations for 1137 selected on small compounds. Based on their average MM/GBSA scores, 18 ligands were selected for 50 ns MD simulations along with one highly potent USP7 inhibitor used as a positive control. The in vitro enzymatic inhibition assay testing of our lead 18 molecules confirmed that 7 of these molecules were successful in USP7 inhibition. Screening results showed that within the used screening approaches, the most successful one was structure-based pharmacophore modeling with the success rate of 75 %. The identification of potent and safe USP7 small molecules as potential inhibitors is a step closer to finding appropriate effective therapies for cancer. Our lead ligands can be used as a scaffold for further structural optimization and development, enabling further research in this promising field.

Toward steroidal anticancer drugs: Non-parametric and 3D-QSAR modeling of 17-picolyl and 17-picolinylidene androstanes with antiproliferative activity on breast adenocarcinoma cells

The present study is aimed to analyze lipophilicity and ADMET profiles, and to develop field based 3D-QSAR and ligand-based pharmacophore hypothesis for a series of 17α-picolyl and 17(E)-picolinylidene androstane derivatives in order to give detailed structural insights and to highlight important binding features of novel androstane derivatives, as compounds with antiproliferative activity toward breast adenocarcinoma cells. This study can provide guidelines for the rational design of novel potent compounds. Sum of ranking differences (SRD), as a non-parametric method, was applied for compounds ranking. 3D-QSAR methods, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were applied to predict the antiproliferative effect on breast adenocarcinoma cells and provide the regions in space where interactive fields may influence the activity. The compounds are ranked so the compounds with the most favorable ADME and lipophilicity features together with significant anticancer activity can be distinguished. The established 3D-QSAR model could be used for design of new compounds with antiproliferative activity on the human ER- breast adenocarcinoma cells. The pharmacophore model is able to accurately predict antiproliferative activity. Generally, the present study provides significant guidelines for further selection, synthesis and rational design of new highly potential androstane derivatives as anticancer drugs.

Pharmacophore feature prediction and molecular docking approach to identify novel anti-HCV protease inhibitors

Discovering a potential drug for HCV treatment is a challenging task in the field of drug research. This study initiates with computational screening and modeling of promising ligand molecules. The foremost modeling method involves the identification of novel compound and its molecular interaction based on pharmacophore features. A total of 197 HCV compounds for NS3/4A protein target were screened for our study. The pharmacophore models were generated using PHASE module implemented in Schrodinger suite. The pharmacophore features include one hydrogen bond acceptor, one hydrogen bond donor, and three hydrophobic sites. As a result, based on mentioned hypothesis the model ADHHH.159 corresponds to the CID 59533233. Furthermore, docking was performed using maestro for all the 197 compounds. Among these, the CID 59533313 and 59533233 possess the best binding energy of -11.75 and -10.40 kcal/mol, respectively. The interactions studies indicated that the CID complexed with the NS3/4A protein possess better binding affinity with the other compounds. Further the compounds were subjected to calculate the ADME properties. Therefore, it can be concluded that these two compounds could be a potential alternative drug for the development of HCV.

Design and synthesis of VEGFR-2 tyrosine kinase inhibitors as potential anticancer agents by virtual based screening

Design and synthesis of VEGFR-2 tyrosine kinase inhibitors as potential anticancer agents has been done by a virtual based screening approach.

X-ray crystallographic structures as a source of ligand alignment in 3D-QSAR

Three-dimensional quantitative structure-activity relationships (3D-QSAR) analyses are methods correlating a pharmacological property with a mathematical representation of a molecular property distribution around three-dimensional molecular models for a set of congeners. 3D-QSAR methods are known to be highly sensitive to ligand conformation and alignment method. The current study collects 32 unique positions of congeneric ligands co-crystallized with the binding domain of AMPA receptors and aligns them using protein coordinates. Thus, it allows for a unique opportunity to consider a ligands' orientation aligned by their mode of binding in a native molecular target. Comparative molecular field analysis (CoMFA) models were generated for this alignment and compared with the results of analogous modeling using standard structure-based alignment or obtained in docking simulations of the ligands' molecules. In comparison with classically derived models, the model based on X-ray crystallographic studies showed much better performance and statistical significance. Although the 3D-QSAR methods are mainly employed when crystallographic information is limited, the current study underscores the importance that the selection of inappropriate molecular conformations and alignment methods can lead to generation of erroneous models and false conclusions.

3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.

Computational methods for the discovery of mood disorder therapies

INTRODUCTION

Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 - 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators.

AREAS COVERED

This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling.

EXPERT OPINION

While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.

Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors

Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r(2) = 0.92, SD = 0.16, F = 84.8, N = 40) and for test set (Q(2) = 0.71, RMSE = 0.06, Pearson R = 0.90, N = 10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.

Pharmacophore mapping and electronic feature analysis for a series of nitroaromatic compounds with antitubercular activity

A five point pharmacophore was generated using PHASE for a series of nitroaromatic compounds and their congeners as antitubercular agents. The generated pharmacophore yielded significant 3D-QSAR model with r(2) of 0.890 for a training set of 92 molecules. The model also showed excellent predictive power with correlation coefficient Q(2) of 0.857 for a test set of 31 compounds. The pharmacophore indicated that presence of a nitro group, a piperazine moiety, one aromatic ring feature and two acceptor features are necessary for potent antitubercular activity. The pharmacophore was supported by electronic property analysis using density functional theory (DFT) at B3LYP/3-21*G level. Molecular electrostatic profile of the compounds was consistent with the generated pharmacophore model, particularly appearance of localized negative potential regions near both the oxygen atoms of nitro group extending laterally to the isoxazole ring system/amide bond in the most active compounds. Calculated data further revealed that all active compounds have smaller LUMO energies located over the nitro group, furan ring, and isoxazole ring/amide bond attached to it. Higher negative values of LUMO energies concentrated over the nitro group are indicative of the electron acceptor capacity of the compounds, suggesting that these compounds are prodrugs and must be activated by TB-nitroreductase. The results obtained from this study should aid in efficient design and development of nitroaromatic compounds as antitubercular agents.

Refinement and 3D-QSAR Studies of Inhibitors of Cyclophilin A Containing Amide Linker

Cyclophilin A (CypA) is a ubiquitous cellular enzyme playing essential role in many biological processes, and the discovery of CypA inhibitor is now of special interest in the treatment of immunological disorders. In this work, molecular modeling studies were performed to develop a predictive Common Pharmacophore Hypothesis (CPH) and use it for alignment in 3D-QSAR studies using CoMFA and CoMSIA. A total of 30 compounds containing an amide fragment as the key linker, consisting of 17 of our previously discovered CypA inhibitors and 13 other inhibitors reported in the literature, were selected for pharmacophore refinement and 3D-QSAR studies. The best pharmacophore hypothesis AADR, which had two hydrogen bond acceptors, a hydrogen bond donor, and an aromatic ring, was obtained and used for the alignment of molecules in CoMFA and CoMSIA model development. The models showed a good r 2 value of 0.992 and 0.949 for CoMFA and CoMSIA, respectively. The contour maps of the models were analyzed to give structural insight for activity improvement of future novel CypA inhibitors. The CPH can also provide a powerful template for virtual screening and design of new CypA inhibitors.