Inhibitors of HIV protease: Unique non-peptide active site templates

New templates were designed and prepared which straddle the active site of HIV-1 protease. These templates were designed to be ‘flexible scaffolds’ upon which substituents could be appended to fill the pockets of HIV protease. The new templates prepared and analysed were 4-hydroxy-5H-furan-2-ones, 4-hydroxy-5,6-dihydropyrones, 3-hydroxy-cyclohex-2-enones, and 4-hydroxy-2(1H)-pyridinones, of which the 4-hydroxy- 5,6-dihydropyrones were found to be the most potent inhibitors of HIV-1 protease.     

QSAR-based prediction of anti-HCV activity of thiourea derivatives

Quantitative structure–activity relationship (QSAR) studies were performed on a series of thioureas to explore the physico-chemical parameters responsible for their activity against the hepatitis C virus (HCV)-infected AVa5 cell. The physico-chemical parameters were calculated using WIN CAChe 6.1. Multiple linear regression analysis, after the variables selection by factor analysis, was performed to derive QSAR models which were further evaluated for their statistical significance and predictive power by internal and external validation. The developed QSAR model had the correlation coefficient (R) = 0.928 and cross-validated squared correlation coefficient (Q ²) = 0.751. The selected significant QSAR model indicates that hydrophobicity, dielectric energy, valence connectivity index (order 1), conformational minimum energy and highest occupied molecular orbital of the whole molecule play an important role in the anti-HCV activity of thioureas.     

From SAR to comparative QSAR: role of hydrophobicity in the design of 4-hydroxy-5,6-dihydropyran-2-ones HIV-1 protease inhibitors

Role of hydrophobicity in the design of 4-hydroxy-5,6-dihydropyran-2-ones-a new class of emerging HIV-1 protease inhibitors (HIV-PI) was investigated by using comparative QSAR. These studies show that most of the data points in the individual dataset studied fall either on positive or negative side of the optimum value of ClogP. This is why, we observe either a positive or negative ClogP term in the QSAR. To observe the optimum value of ClogP for these inhibitors, a sufficient spread in the data is required. It is hoped that the results of this study would help in optimizing substituents for better binding at enzyme pockets and guide in the design of more effective HIV-PI of this class.     

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r_loo² values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r² values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r_pred²) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r_pred² of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.     

4-hydroxy-5,6-dihydro-2H-pyran-2-ones.3. Bicyclic and hetero-aromatic ring systems as 3-position scaffolds to bind to S1' and S2' of the HIV-1 protease enzyme.

5,6-Dihydro-2H-pyran-2-ones are potent inhibitors of HIV-1 protease, which bind to the S1, S2, S1', and S2' pockets and have a unique binding mode with the catalytic aspartyl groups and the flap region of the enzyme. Efforts to explore 3-position heterocyclic scaffolds that bind to the S1' and S2' pockets have provided a number of selected analogs that display high HIV-1 protease inhibitory activity. reserved.     

Design, synthesis, and biological evaluation of novel 4-hydroxypyrone derivatives as HIV-1 protease inhibitors

Twenty-four 4-hydroxypyrone derivatives were synthesized with a facile synthetic method to develop novel HIV protease inhibitors. Most of them were shown to display good antiviral activities in SIV-infected CEM cells. The introduction of alpha-naphthylmethyl group to C-6 of 5,6-dihydropyran-2-ones led to an effective antiviral compound that showed an EC(50) value at 1.7 microM with a therapeutic index of 46.     

Molecular docking and 3D-QSAR studies of HIV-1 protease inhibitors

HIV-1 protease is an obligatory enzyme in the replication process of the HIV virus. The abundance of structural information on HIV-1PR has made the enzyme an attractive target for computer-aided drug design strategies. The daunting ability of the virus to rapidly generate resistant mutants suggests that there is an ongoing need for new HIV-1PR inhibitors with better efficacy profiles and reduced toxicity. In the present investigation, molecular modeling studies were performed on a series of 54 cyclic urea analogs with symmetric P2/P2′ substituents. The binding modes of these inhibitors were determined by docking. The docking results also provided a reliable conformational superimposition scheme for the 3D-QSAR studies. To gain insight into the steric, electrostatic, hydrophobic and hydrogen-bonding properties of these molecules and their influence on the inhibitory activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed. Two different alignment schemes viz. receptor-based and atom-fit alignment, were used in this study to build the QSAR models. The derived 3D-QSAR models were found to be robust with statistically significant r ² and r ² _pred values and have led to the identification of regions important for steric, hydrophobic and electronic interactions. The predictive ability of the models was assessed on a set of molecules that were not included in the training set. Superimposition of the 3D-contour maps generated from these models onto the active site of enzyme provided additional insight into the structural requirements of these inhibitors. The CoMFA and CoMSIA models were used to design some new inhibitors with improved binding affinity. Pharmacokinetic and toxicity predictions were also carried out for these molecules to gauge their ADME and safety profile. The computational results may open up new avenues for synthesis of potent HIV-1 protease inhibitors.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.     

Quantitative structure-activity relationship by CoMFA for cyclic urea and nonpeptide-cyclic cyanoguanidine derivatives on wild type and mutant HIV-1 protease

Abstact 3D-QSAR studies using the Comparative Molecular Field Analysis (CoMFA) methodology were conducted to predict the inhibition constants, K_i, and the inhibitor concentrations, IC₉₀ of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. A significant cross-validated correlation coefficient (q²) of 0.63 and a fitted correlation coefficient r² of 0.70 were obtained, indicating that the models can predict the anti-protease activity from poorly to highly active compounds reliably. The best predictions were obtained for: XV643 (predicted log 1/K_i=9.86), a 3,5-dimethoxy-benzyl cyclic urea derivate (molec60, predicted log 1/K_i=8.57) and a benzyl cyclic urea derivate (molec 61, predicted log 1/IC₉₀=6.87). Using the CoMFA method, we also predicted the biological activity of 14 cyclic urea derivatives that inhibit the HIV-1 protease mutants V82A, V82I and V82F. The predicted biological activities of the: (i) XNO63 (inhibitory activity on the mutant HIV-1 PR V82A), (ii) SB570 (inhibiting the mutant HIV-1 PR V82I) and also (iii) XV652 (during the interaction with the mutant HIV-1 PR V82F) were in good agreement with the experimental values.Figure Stereoview of the contour plots of the CoMFA steric and electrostatic fields. The favorable (indicated by blue polyhedra) and unfavorable (represented by red polyhedra) electrostatic areas and also the favorable (shown by green polyhedra) and unfavorable (shown by yellow polyhedra) steric areas formed around the most active molecule, 6a.     

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r² of 0.996; for the test set, the correlation coefficient r² was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.     

Docking and 3D QSAR study of thiourea analogs as potent inhibitors of influenza virus neuraminidase

Surflex-Dock was applied to study interactions between 30 thiourea analogs and neuraminidase (NA). The docking results showed that hydrogen bonding and electrostatic interactions were highly correlated with the activities of neuraminidase inhibitors (NIs), followed by hydrophobic and steric factors. Moreover, there was a strong correlation between the predicted binding affinity (total score) and experimental pIC₅₀ (correlation coefficient r = 0.870; P < 0.0001). A three dimensional holographic vector of atomic interaction field (3D-HoVAIF) was employed to construct a QSAR model. The r ², q ² and r ² _test values of the optimal QSAR model were 0.849, 0.724 and 0.689, respectively. From the QSAR model, it could be seen that electrostatic, hydrophobic and steric interactions were closely related to inhibitory activity, which was consistent with the docking results. Based on the docking and QSAR results, five new compounds with high predicted activities were designed.     

A mechanistic study of 3-aminoindazole cyclic urea HIV-1 protease inhibitors using comparative QSAR

Comparative QSAR studies on P2/P2' and P1/P1' substituted symmetrical and nonsymmetrical 3-aminoindazole cyclic urea HIV-1 protease inhibitors were performed. The protease inhibitory activity of these compounds was found to decrease with larger and more hydrophobic molecules, whereas the antiviral potency and translation across the cell membrane increases with increase in hydrophobicity and size. These results provide mechanistic insight about the mode of interaction of these compounds with HIV-1 protease receptor and would help in further improving the biological activity.     

A quantitative structure-activity relationship study for structurally diverse HIV-1 protease inhibitors: contribution of conformational flexibility to inhibitory activity

In this study, we investigated by linear regression model the SAR data of the 15 HIV-1 protease inhibitors possessing structurally diverse scaffolds. First, a regression model was developed only using the enzyme-inhibitor interaction energy as a term of the model, but did not provide a good correlation with the inhibitory activity (R² = 0.580 and Q² = 0.500). Then, we focused on the conformational flexibility of the inhibitors which may represent the diversity of the inhibitors, and added two conformational parameters into the model, respectively: the number of rotatable bonds of ligands (ΔSrot) and the distortion energy of ligands (ΔElig). The regression model by adding ΔElig successfully improved the quality of the model (R² = 0.771 and Q² = 0.713) while the model with ΔSrot was unsuccessful. The prediction for a training inhibitor by the ΔElig model also showed good agreement with experimental activity. These results suggest that the conformational flexibility of HIV-1 protease inhibitors directly contributes to the enzyme inhibition.     

A combination of pharmacophore modeling,atom-based 3D-QSAR,molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors

Phosphodiesterases 4 enzyme is an attractive target for the design of anti-inflammatory and bronchodilator agents. In the present study, pharmacophore and atom-based 3D-QSAR studies were carried out for pyrazolopyridine and quinoline derivatives using Schrödinger suite 2014-3. A four-point pharmacophore model was developed using 74 molecules having pIC₅₀ ranging from 10.1 to 4.5. The best four feature model consists of one hydrogen bond acceptor, two aromatic rings, and one hydrophobic group. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R^2?=?.9949), cross validation coefficient (Q^2?=?.7291), and Pearson-r (.9107) at six component partial least square factor. The external validation indicated that our QSAR model possessed high predictive power with R² value of .88. The generated model was further validated by enrichment studies using the decoy test. Molecular docking, free energy calculation, and molecular dynamics (MD) simulation studies have been performed to explore the putative binding modes of these ligands. A 10-ns MD simulation confirmed the docking results of both stability of the 1XMU–ligand complex and the presumed active conformation. Outcomes of the present study provide insight in designing novel molecules with better PDE4 inhibitory activity.