Synthesis, anti-tuberculosis activity, and 3D-QSAR study of 4-(adamantan-1-yl)-2-substituted quinolines

Structural optimization of the previously identified 4-(adamantan-1-yl)-2-quinolinecarbohydrazide (AQCH, MIC=6.25 microg/mL, 99% inhibition, Mycobacterium tuberculosis H37Rv) has led to two series of 4-(adamantan-1-yl)-2-substituted quinolines (Series 1-2). All new derivatives were evaluated in vitro for antimycobacterial activities against drug-sensitive M. tuberculosis H37Rv strain. Several 4-adamantan-1-yl-quinoline-2-carboxylic acid N'-alkylhydrazides (Series 1) described herein showed promising inhibitory activity. In particular, analogs 7, 9, 20, and 21 displayed MIC of 3.125 microg/mL. Further investigation of AQCH by its reaction with various aliphatic, aromatic, and heteroaromatic aldehydes led to the synthesis of 4-adamantan-1-yl-quinoline-2-carboxylic acid alkylidene hydrazides (Series 2). Analogs 42-44 and 48 have produced promising antimycobacterial activities (99% inhibition) at 3.125 microg/mL against drug-sensitive M. tuberculosis H37Rv strain. The most potent analog 35 of the series produced 99% inhibition at 1.00 microg/mL against drug-sensitive strain, and MIC of 3.125 microg/mL against isoniazid-resistant TB strain. To understand the relationship between structure and activity, a 3D-QSAR analysis has been carried out by three methods-comparative molecular field analysis (CoMFA), CoMFA with inclusion of a hydropathy field (HINT), and comparative molecular similarity indices analysis (CoMSIA). Several statistically significant CoMFA, CoMFA with HINT, and CoMSIA models were generated. Prediction of the activity of a test set of molecules was the best for the CoMFA model generated with database alignment. Based on the CoMFA contours, we have tried to explain the structure-activity relationships of the compounds reported herein.     

3-D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists. The best CoMFA and CoMSIA models that were generated using atom based alignment from a training set of twenty five tetrahydrofuroyl-L-phenylalanine derivatives, are six-component models with good statistics; CoMFA: r(2)(cv)=0.366, r(2)=0.983, s=0.099, F=172.661 and PRESS=4.435; CoMSIA: r(2)(cv)=0.528, r(2)=0.995, s=0.054, F=577.87 and PRESS=3.563. Both of these 3-D-QSAR models were validated using a test set of eleven compounds, whose predicted pIC(50) values fall within one log unit of the actual pIC(50). The contour diagrams obtained for the various CoMFA and CoMSIA field contributions can be mapped back onto structural features to explain the activity trends of the molecules analysed. Based on the spatial arrangement of the various field contributions, novel molecules with improved activity can be designed.     

3D-QSAR study of 8-azabicyclo[3.2.1] octane analogs antagonists of cholinergic receptor

3D-QSAR models of Comparative of Molecular Field Analysis (CoMFA) and Comparative of Molecular Similarities Indices Analysis (CoMSIA) of 20 8-azabicyclo[3.2.1] octane (potent muscarinic receptor blocker) was performed. These benztropine analogs were optimized using ligand based alignment method. The conventional ligand-based 3D-QSAR studies were performed based on the low energy conformations employing database alignment rule. The ligand-based model gave q² value 0.819 and 0.810 and r² value 0.991 and 0.988 for CoMFA and CoMSIA, respectively, and the predictive ability of the model was validated. Results indicate that the CoMFA and CoMSIA models could be reliable model which may be used in the design of novel muscarinic antagonists as leads.     

Three-dimensional quantitative structure-activity relationship analyses of piperidine-based CCR5 receptor antagonists

The CCR5 chemokine receptor has recently been found to play a crucial role in the viral entry stage of HIV infection and has therefore become an attractive potential target for anti-HIV therapeutics. On the other hand, the lack of CCR5 crystal structure data has impeded the development of structure-based CCR5 antagonist design. In this paper, we compare two three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) on a series of piperidine-based CCR5 antagonists as an alternative approach to investigate the interaction between CCR5 antagonists and their receptor. Superimposition of antagonist structures was performed using two alignment rules: atomic/centroid rms fit and rigid body field fit techniques. The 3D QSAR models were derived from a training set of 72 compounds, and were found to have predictive capability for a set of 19 holdout test compounds. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to biological activity in this series of compounds. Further analyses of these interaction-field contour maps also showed a high level of internal consistency.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.     

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

Two 3D-QSAR methods--CoMFA and CoMSIA--were applied to a set of 38 angiotensin receptor (AT1) antagonists. The conformation and alignment of molecules were obtained by a novel method - consensus dynamics. The representation of biological activity, partial charge formalism, absolute orientation of the molecules in the grid, and grid spacing were also studied for their effect on the CoMFA models. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The best CoMFA model had a cross-validated correlation coefficient ( q2) of 0.632, which improved with "region focusing" to 0.680. This model had a "predictive" r2 of 0.436 on a test series that was unique and with little representation in the training set. Although the "predictive" r2 of the best CoMSIA model, which included steric, electrostatic, and hydrogen bond acceptor fields was higher than that of the best CoMFA model, the other statistical parameters like q2, r2, F value, and s were unsatisfactory. The contour maps generated using the best CoMFA model were used to identify the structural features important for biological activity in these compounds.     

3D-QSAR CoMFA, CoMSIA studies on substituted ureas as Raf-1 kinase inhibitors and its confirmation with structure-based studies

Three-dimensional quantitative structure activity relationship (3D-QSAR) analyses were carried out on 91 substituted ureas in order to understand their Raf-1 kinase inhibitory activities. The studies include Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Models with good predictive abilities were generated with the cross validated r2 (r2cv) values for CoMFA and CoMSIA being 0.53 and 0.44, respectively. The conventional r2 values are 0.93 and 0.87 for CoMFA and CoMSIA, respectively. In addition, a homology model of Raf-1 was also constructed using the crystal structure of the kinase domain of B-Raf isoform with one of the most active Raf-1 inhibitors (48) inside the active site. The ATP binding pocket of Raf-1 is virtually similar to that of B-Raf. Selected ligands were docked in the active site of Raf-1. Molecule 48 adopts an orientation similar to that inside the B-Raf active site. The 4-pyridyl group bearing amide substituent is located in the adenosine binding pocket, and anchored to the protein through a pair of hydrogen bonds with Cys424 involving ring N-atom and amide NH group. The results of best 3D-QSAR model were compared with structure-based studies using the Raf-1 homology model. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as Raf-1 inhibitors. Based on these results, novel molecules with improved activity can be designed.     

3D QSAR studies on T-type calcium channel blockers using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of isoxazolyl compounds as a potent T-type calcium channel blockers. A set of 24 structurally similar compounds served to establish the model. Four different conformations of the most active compound were used as template structures for the alignment, three of which were obtained from Catalyst pharmacophore modeling and one by using SYBYL random search option. All CoMFA and CoMSIA models gave cross-validated r(2) (q(2)) value of more than 0.5 and conventional r(2) value of more than 0.85. The predictive ability of the models was validated by an external test set of 10 compounds, which gave satisfactory pred r(2) values ranging from 0.577 to 0.866 for all models. Best predictions were obtained with CoMFA std model of Conformer no: 3 alignment (q(2)=0.756, r(2)=0.963), giving predictive r(2) value of 0.866 for the test set. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands accounting for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, hydrophobic and hydrogen bonding fields.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT(7) 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 5HT(7) 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 q(2) (cross validated correlation coefficient) of 0.642, 0.602 and r(2) (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT(7) antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r(2) 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.     

Three-dimensional quantitative structure-activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of benzotriazine derivatives, as Src inhibitors. Ligand molecular superimposition on the template structure was performed by database alignment method. The statistically significant model was established of 72 molecules, which were validated by a test set of six compounds. The CoMFA model yielded a q(2)=0.526, non cross-validated R(2) of 0.781, F value of 88.132, bootstrapped R(2) of 0.831, standard error of prediction=0.587, and standard error of estimate=0.351 while the CoMSIA model yielded the best predictive model with a q(2)=0.647, non cross-validated R(2) of 0.895, F value of 115.906, bootstrapped R(2) of 0.953, standard error of prediction=0.519, and standard error of estimate=0.178. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. Results indicate that small steric volumes in the hydrophobic region, electron-withdrawing groups next to the aryl linker region, and atoms close to the solvent accessible region increase the Src inhibitory activity of the compounds. In fact, adding substituents at positions 5, 6, and 8 of the benzotriazine nucleus were generated new compounds having a higher predicted activity. The data generated from the present study will further help to design novel, potent, and selective Src inhibitors as anticancer therapeutic agents.     

3D-QSAR studies of heterocyclic quinones with inhibitory activity on vascular smooth muscle cell proliferation using pharmacophore-based alignment

The abnormal proliferation and migration of vascular smooth muscle cells (SMCs) play an important role in the pathology of coronary artery atherosclerosis and restenosis following angioplasty. It was reported that some heterocyclic quinone derivatives such as 6-arylamino-quinoxaline-5,8-diones and 6-arylamino-1H-benzo[d]imidazole-4,7-diones have inhibitory activity on rat aortic smooth muscle cell (RAoSMC) proliferation. To understand the structural basis for antiproliferative activity to design more potent agents, we generated pharmacophore models of representative molecules with high activity using Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD) and aligned a series of compounds to the selected pharmacophore model, then performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Good cross-validated correlations were obtained with CoMFA (resulting in q(2) of 0.734 and r(2) of 0.947) and CoMSIA (resulting in q(2) of 0.736 and r(2) of 0.913). The IC(50) values of the heterocyclic quinone derivatives on RAoSMC exhibited a strong correlation with steric and hydrophobic fields of the 3D structure of the molecules, resulting in the reliable prediction of inhibitory activity of the series of compounds.     

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.     

Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening

A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the “Thumb Pocket 2” allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.     

CoMFA and CoMSIA 3D-QSAR analysis on hydroxamic acid derivatives as urease inhibitors

Urease (EC 3.5.1.5) serves as a virulence factor in pathogens that are responsible for the development of many diseases in humans and animals. Urease allows soil microorganisms to use urea as a source of nitrogen and aid in the rapid break down of urea-based fertilizers resulting in phytopathicity. It has been well established that hydroxamic acids are the potent inhibitors of urease activity. The 3D-QSAR studies on thirty five hydroxamic acid derivatives as known urease inhibitors were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The CoMFA model produced statistically significant results with cross-validated (q(2)) 0.532 and conventional (r(2)) correlation coefficients 0.969.The model indicated that the steric field (70.0%) has greater influence on hydroxamic acid inhibitors than the electrostatic field (30.0%). Furthermore, five different fields: steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor assumed to generate the CoMSIA model, which gave q(2) 0.665 and r(2) 0.976.This model showed that steric (43.0%), electrostatic (26.4%) and hydrophobic (20.3%) properties played a major role in urease inhibition. The analysis of CoMFA and CoMSIA contour maps provided insight into the possible modification of the hydroxamic acid derivatives for improved activity.     

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.     

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.     

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

Docking-based 3D-QSAR (CoMFA,CoMFA-RG,CoMSIA) study on hydroquinoline and thiazinan-4-one derivatives as selective COX-2 inhibitors

A series of 26 selective COX-2 inhibitors which reported previously by our laboratory was selected to generate three-dimensional quantitative structure activity relationship (3D-QSAR) model. Active conformation of each molecule was predicted by docking studies and used for molecular alignment. Activity of 20 molecules as a train set was predicted using three methods including comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RG) and comparative molecular similarity index analysis (CoMSIA). The best models of CoMFA-RG and CoMSIA revealed correlation coefficients r² of 0.955 and 0.947, the leave one out cross-validation coefficients q² of 0.573 and 0.574, respectively. In addition, CoMFA-RG and CoMSIA models were validated by a test set of six molecules with predicted coefficients r²_pred of 0.644 and 0.799, respectively. Contour maps of generated models provided fruitful information about structural aspect of molecules that affected their COX-2 inhibitory activity. Based on three models results, steric and electrostatic properties are the most important factors in controlling the activity of the molecules. Results of CoMFA-RG and CoMSIA models were utilized to design new molecules. Comparison of experimental and predicted pIC₅₀ values of designed molecules indicated that CoMFA-RG had the more predictive ability.

Communicated by Ramaswamy H. Sarma     

The 3-D QSAR study of anticancer 1-N-substituted imidazo- and pyrrolo-quinoline-4,9-dione derivatives by CoMFA and CoMSIA

The 3-D QSAR analysis with new imidazo- and pyrrolo-quinolinedione derivatives was conducted by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). When crossvalidation value (q(2)) is 0.844 at four components, the Pearson correlation coefficient (r(2)) of the CoMFA is 0.964. In the CoMSIA, q(2) is 0.709 at six components and r(2) is 0.969. Unknown samples were analyzed, using QSAR analyzed results from the CoMFA and CoMSIA methods. Excellent agreement was obtained between, with an error range of 0.01-0.15 the calculated values and measured in vitro cytotoxic activities against human lung A-549 cancer cell lines.