Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques

Computational discovery of novel anthelmintic natural compounds from Agave Brittoniana trel. Spp. Brachypus

Helminth infections are a medical problem in the world nowadays. This report used bond-based 2D quadratic indices, a bond-level QuBiLs-MAS molecular descriptor family, and Linear Discriminant Analysis (LDA) to obtain a quantitative linear model that discriminates between anthelmintic and non-anthelmintic drug-like organic-compounds. The model obtained correctly classified 87.46% and 81.82% of the training and external data sets, respectively. The developed model was used in a virtual screening to predict the biological activity of all chemicals (19) previously obtained and chemically characterized by some authors of this report from Agave brittoniana Trel. spp. Brachypus. The model identified several metabolites (12) as possible anthelmintics, and a group of 5 novel natural products was tested in an in vitro assay against Fasciola hepatica (100% effectivity at 500 µg/mL). Finally, the two best hits were evaluated in vivo in bald/c mice and the same helminth parasite using a 25 mg/kg dose. Compound 8 (Karatavinoside A) showed an efficacy of 92.2% in vivo. It is important to remark that this natural compound exhibits similar-to-superior activity as triclabendazole, the best human fasciolicide available in the market against Fasciola hepatica, resulting in a novel lead scaffold with anti-helminthic activity. Keywords: TOMOCOMD-CARDD Software; QuBiLs-MAS, nonstochastic and stochastic bond-based quadratic indices; LDA-based QSAR model; Computational Screening, Anthelmintic Agent; Agave brittoniana Trel. spp. Brachypus, Fasciola hepatica.

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.

Computational study on the interaction between CCR5 and HIV-1 entry inhibitor maraviroc: insight from accelerated molecular dynamics simulation and free energy calculation

C-C chemokine receptor type 5 (CCR5) is the co-receptor of human immunodeficiency virus type 1 (HIV-1) and plays an important role in HIV-1 virus infection. Maraviroc has been proved to be effective for anti-HIV-1 by targeting CCR5. Understanding the detailed interaction mechanism between CCR5 and Maraviroc will be of great help to the rational design of a more potential inverse agonist to block HIV-1 infection. Here, we performed molecular dynamics (MD) simulation and accelerated MD simulation (aMD) to study the interaction mechanism between CCR5 and Maraviroc based on a recently reported crystal structure. The results of MD simulation demonstrate that Maraviroc can form stable hydrogen bonds with residues Tyr37(1.39), Tyr251(6.51) and Glu283(7.39). The results of aMD simulation indicate that the carboxamide moiety is more flexible than the tropane group of Maraviroc in the pocket of CCR5. The electrostatic potential analysis proves that Maraviroc can escape from the pocket of CCR5 along the negative electrostatic potential pathway during the dissociation process. The free energy calculation illustrates that there exist three binding pockets during the dissociation process of Maraviroc. Our results will be useful for understanding the interaction mechanism between CCR5 and Maraviroc as well as for the rational design of a more potent inverse agonist.

Integrated computational tools for identification of CCR5 antagonists as potential HIV-1 entry inhibitors: homology modeling, virtual screening, molecular dynamics simulations and 3D QSAR analysis

Using integrated in-silico computational techniques, including homology modeling, structure-based and pharmacophore-based virtual screening, molecular dynamic simulations, per-residue energy decomposition analysis and atom-based 3D-QSAR analysis, we proposed ten novel compounds as potential CCR5-dependent HIV-1 entry inhibitors. Via validated docking calculations, binding free energies revealed that novel leads demonstrated better binding affinities with CCR5 compared to maraviroc, an FDA-approved HIV-1 entry inhibitor and in clinical use. Per-residue interaction energy decomposition analysis on the averaged MD structure showed that hydrophobic active residues Trp86, Tyr89 and Tyr108 contributed the most to inhibitor binding. The validated 3D-QSAR model showed a high cross-validated r_cv² value of 0.84 using three principal components and non-cross-validated r² value of 0.941. It was also revealed that almost all compounds in the test set and training set yielded a good predicted value. Information gained from this study could shed light on the activity of a new series of lead compounds as potential HIV entry inhibitors and serve as a powerful tool in the drug design and development machinery.

A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection

Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.

Atherogenic effect of Arecoline: A computational study

There are over 600 million people worldwide covering Asian and Oceanic countries including India have the habit of chewing areca nut as masticator in different forms. Arecoline (C(8)H(13)NO(2)) has been reported as one of the abundant constituents of areca nut. A good number of scientific publications have made Arecoline responsible for oral cancer. Based on observation from clinical situation in North East India, one of the most betel quid chewing region of the country, we suspected a link between consumption of areca nut and Cerebro Vascular Disease like stroke. Therefore, we considered Low Density Lipoprotein (LDL) receptor as target and Arecoline as ligand and studied ligand -target interaction using computational tools. Also we considered High Density Lipoprotein (HDL) receptor as another target to see if Arecoline has any binding potential with it over and above LDL receptor. Docking result indicated that Arecoline and Cholesterol both, have affinity towards extracellular domain of Human LDL receptor but affinity of Arecoline is much higher (-12.3560.) than that of Cholesterol(-0.1810). Docking of Arecoline and 1, 2-Hexyl-1- cyclopentanone thiosemicarbazone (thiosemicarbazone) with Bovine HDL receptor showed that Arecoline also has the potential (Score, -6.2690Kcal/Mol) to block HDL receptor though its potential is less than that (score, -10.0509 Kcal/Mol) of control (thiosemicarbazone). We, therefore, suggest that by inhibiting endocytosis of LDL cholesterol because of blocking LDL receptor function and also by preventing LDL cholesterol uptake by liver from blood because of interference with HDL receptor, Arecoline may contribute to atherosclerosis. The study therefore, indicates a positive correlation between chewing of betel quid and Cerebro Vascular Disease.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening

The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC₅₀ of CXCR2 receptors for which no experimental data is available.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.

Identification of a series of novel derivatives as potent HCV inhibitors by a ligand-based virtual screening optimized procedure

This paper presents the results of a ligand-based virtual screening optimized procedure on 98 compounds which have been recently evaluated as inhibitors of genotype 1 HCV polymerase. First, quantitative structure-activity patterns are investigated for the selected compounds and then structural modifications are proposed to afford novel active patterns. An accurate and reliable QSAR model involving five descriptors that is able to predict successfully the HCV inhibitory potency against genotype 1 HCV polymerase is presented. Furthermore, the effects of various structural modifications on biological activity are investigated and biological activities of novel structures are estimated using the developed QSAR model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.

2D Quantitative structure-activity relationship studies on a series of cholesteryl ester transfer protein inhibitors

Coronary heart disease (CHD) is one of the major causes of human death. The most successful therapeutic approach available is based on the reduction of low density-lipoprotein cholesterol (LDL-C). However, it is believed that the next paradigm in CHD treatment will rely on increased HDL-C levels. One of the most promising strategies for this goal is the inhibition of cholesteryl ester transfer protein (CETP). In the present work, robust classical 2D QSAR (r(2)=0.76, q(2)=0.72) and hologram QSAR (r(2)=0.88, q(2)=0.70) models were developed for a series of 85 CETP inhibitors (N-N-disubstituted trifluoro-3-amino-2-propanol derivatives). These models are complementary in nature and highlight important structural features for the design of novel CETP inhibitors with improved potency.

Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening

This paper presents the results of an optimization study on biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists, which was accomplished by using quantitative-structure activity relationships (QSARs), classification and virtual screening techniques. First, a linear QSAR model was developed using Multiple Linear Regression (MLR) Analysis, while the Elimination Selection-Stepwise Regression (ES-SWR) method was adopted for selecting the most suitable input variables. The predictive activity of the model was evaluated using an external validation set and the Y-randomization technique. Based on the selected descriptors, the Support Vector Machines (SVM) classification technique was utilized to classify data into two categories: "actives" or "non-actives". Several attempts were made to optimize the scaffold of most potent compounds by inducing various structural modifications. Potential derivatives with improved activities were identified, as they were classified "actives" by the SVM classifier. Their activities were estimated using the produced MLR model. A detailed analysis on the model applicability domain defined the compounds, whose estimations can be accepted with confidence.

3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists

3D-QSAR studies on the derivatives of 1-(3,3-diphenylpropyl)-piperidinyl amide and urea as CCR5 receptor antagonists were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule, the most active and pharmacokinetically stable molecule of the series, was obtained by systematic search and used to build structures of the molecules in the dataset. The best predictions for the CCR5-receptor were obtained with the CoMFA standard model (q (2) = 0.787, r (2) = 0.962) and CoMSIA model combined steric, electrostatic and hydrophobic fields (q (2) = 0.809, r (2) = 0.951). The predictive ability of CoMFA and CoMSIA were determined using a test set of 12 compounds giving predictive correlation coefficients of 0.855 and 0.83, respectively, indicating good predictive power. Further, the robustness of the model was verified by bootstrapping analysis. The contour maps produced by the CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series. Based on the CoMFA and CoMSIA analysis, we have identified some key features in the series that are responsible for CCR5 antagonistic activity which may be used to design more potent 1-(3,3-diphenylpropyl)-piperidinyl derivatives and predict their activity prior to synthesis.

Advances in virtual screening

Although the term virtual screening as the in silico analog of high throughput screening has been coined only a decade ago, virtual screening is now a widespread lead identification method in the pharmaceutical industry. A myriad of different methods have been developed exploiting the growing library of target structures and assay data as a basis for finding new lead structures. Exploiting synergies between different methods best utilizes the information available and is at the center of recent developments.