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.     

Three-dimensional quantitative structure-activity relationships of pyrrolopyridinone as cell division cycle kinase inhibitors by CoMFA and CoMSIA

Seventy-five 1,5,6,7-tetrahydro-pyrrolo[3,2-C]pyridinone derivatives displaying potent activities against Cdc7 kinase were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated as well as some measures including region focusing, progressive scrambling, bootstraping and leave-group-out. The satisfactory CoMFA model predicted a q ² value of 0.836 and an r ² value of 0.950, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor effects, predicted a q ² value of 0.636 and an r ² value of 0.907. The models were graphically interpreted using contour plots which provided insight into the structural requirements for increasing the activity of a compound. The final 3D-QSAR results could be used for rational design of potent inhibitors against Cdc7 kinase.     

A quantitative structure-activity relationship study on some matrix metalloproteinase and collagenase inhibitors

A quantitative structure-activity relationship (QSAR) study is made on some hydroxamic acid-based inhibitors of matrix metalloproteinases (MMPs) and a bacterial collagenase, namely Clostridium histolyticum collagenase (ChC), that also belongs to an MMP family, M-31, using Kier's valence molecular connectivity index (1)chi(v) of the substituents and electrotopological state (E-state) indices of some atoms. The results indicate that out of the four MMPs (MMP-1, MMP-2, MMP-8, and MMP-9) studied, MMP-2 and MMP-9 can be structurally quite similar, but widely differing from MMP-1 and MMP-8 and ChC. For MMP-2 and MMP-9, the inhibition activity of compounds is shown to depend on both (1)chi(v )and E-state indices, while for MMP-1 and MMP-8 it is shown to depend only on E-state indices and for ChC only on (1)chi(v). However, in all the cases, an aromatic group like C(6)F(5) or 3-CF(3)-C(6)H(4) attached to SO(2) moiety in the compounds is indicated to be equally beneficial, due to probably the involvement of fluorine atom(s) in charge-charge interactions with the Zn(2+) ion of the enzymes or in the formation of the hydrogen bonds with some sites of the receptors.     

A quantitative structure-activity relationship study on some series of anthranilic acid-based matrix metalloproteinase inhibitors

A quantitative structure-activity relationship (QSAR) study has been made on four different series of anthranilic acid-based matrix metalloproteinase (MMP) inhibitors, in which two substituted aryl rings, one bearing the hydroxamic acid moiety that binds with the zinc atom of MMPs, are joined through a bridge group of sulfonamide. The QSAR results indicate that the sulfonamide group plays a very important role in the inhibition activity of the inhibitors and that the effectiveness of this sulfonamide group can be increased by the presence at the aryl rings or at the sulfonamide nitrogen itself of nitrogen-containing or some such substituents that can increase the electronic character of the sulfonamide group. The hydrophobic character of the molecules is not found to be of any advantage; rather in most of the cases it is shown to have detrimental effect, suggesting that MMPs provide little opportunity to the inhibitors to have a any hydrophobic interactions with them. On the other hand, polarizability of the molecules has been found to be conducive to activity in some cases. Thus the inhibition mechanism seems to predominantly involve the electronic interactions between the inhibitors and the enzymes.     

Three-dimensional quantitative structure-activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.     

CoMFA and CoMSIA studies on fluorinated hexahydropyrimidine derivatives

3D-QSAR models of a series of fluorinated hexahydropyrimidine derivatives with cytotoxic activities have been developed using CoMFA and CoMSIA. These models provide a better understanding of the mechanism of action and structure–activity relationship of these compounds. By applying leave-one-out (LOO) cross validation study, the best predictive CoMFA model was achieved with 3 as the optimum number of components, which gave rise to a non-cross-validated r² value of 0.978, and standard error of estimate of 0.059, and F value of 144.492. Similarly, the best predictive CoMSIA model was derived with 4 as the number of components, r² value of 0.999, F value of 4381.143, and standard error of estimate, 0.011. The above models will inspire the design and synthesis of novel hexahydropyrimidines with enhanced potency and selectivity.     

Ligand-based CoMFA and CoMSIA studies on chromone derivatives as radical scavengers

The antioxidant activity for a series of chromone compounds, evaluated by DPPH free radical scavenging assay, were subjected to 3D-QSAR studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). All 48 chromone derivatives were geometry optimized by AM1 and HF/6-31G^* calculations. The CoMFA and CoMSIA results were compared between different alignment strategies. The best CoMFA model obtained from HF/6-31G^* optimization with field fit alignment gave cross-validated r² (q²) = 0.821, noncross-validated r² = 0.987, S = 0.095, and F = 388.255. The best CoMSIA model derived from AM1 optimized structures and superimposition alignment gave q² = 0.876, noncross-validated r² = 0.976, S = 0.129, and F = 208.073, including electrostatic, hydrophobic, hydrogen bond donor and acceptor fields. The contour maps provide the fruitful structure–radical scavenging activity relationships which are useful for designing new compounds with higher activity.     

A quantitative structure-activity relationship study on matrix metalloproteinase inhibitors: piperidine sulfonamide aryl hydroxamic acid analogs

A quantitative structure-activity relationship (QSAR) study has been made on a series of piperidine sulfonamide aryl hydroxamic acid analogs acting as matrix metalloproteinase (MMP) inhibitors. The inhibitory potencies of the compounds against two MMPs, MMP-2 and MMP-13, are found to be significantly correlated with the hydrophobic properties of the molecules, suggesting that in both enzymes the hydrophobic interaction is playing a dominant role.     

A quantitative structure-activity relationship study on matrix metalloproteinase inhibitors: Piperidine sulfonamide aryl hydroxamic acid analogs

A quantitative structure-activity relationship (QSAR) study has been made on a series of piperidine sulfonamide aryl hydroxamic acid analogs acting as matrix metalloproteinase (MMP) inhibitors. The inhibitory potencies of the compounds against two MMPs, MMP-2 and MMP-13, are found to be significantly correlated with the hydrophobic properties of the molecules, suggesting that in both enzymes the hydrophobic interaction is playing a dominant role.     

A quantitative structure-activity relationship study of hydroxamate matrix metalloproteinase inhibitors derived from functionalized 4-aminoprolines

A quantitative structure-activity relationship (QSAR) study has been made on the inhibitions of some matrix metalloproteinases (MMPs) by functionalized 4-aminoproline based hydroxamates. Attempts have been made to correlate the inhibition potencies of these hydroxamates with Kier's first-order valence molecular connectivity index ((1)chi(v)) of substituents and electrotopological state (E-state) indices of some atoms. The correlations obtained for the inhibitions of all the enzymes studied, i.e. MMP-1, MMP-2, MMP-3, MMP-7, and MMP-13, were not so uniform, but suggested that in almost all the cases the substituents at the amide nitrogen may be conducive to the activity, though the whole amide group may be sterically unfavourable. Similarly, in most of the cases, the substituens at the phenyl moiety have been found to be beneficial to the inhibition potency and in many cases an electronic role of SO(2) group of the sulfonylphenyl moiety has been indicated.     

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.     

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 phytopathiCIT000y. 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 (q²) 0.532 and conventional (r²) 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 q² 0.665 and r² 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.     

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.     

Anthranilate derivatives as TACE inhibitors: Docking based CoMFA and CoMSIA analyses

Anthranilic acid based derivatives (ANTs) have been identified as a novel class of potent tumor necrosis factor-α converting enzyme (TACE) inhibitors. A computational strategy based on molecular docking studies, followed by CoMFA and CoMSIA analyses has been performed to elucidate the atomic details of the TACE/ANT interactions and also to identify the most important features impacting TACE inhibitory activity of ANTs. The CoMSIA model resulted to be slightly more predictive than CoMFA model, and gave conventional r² 0.991, r_cv² 0.793, q² 0.777, SEE 0.050, F-value 655.610, and r_test² 0.871. The 3D-QSAR field contributions and the structural features of the TACE binding site showed a good correlation. These studies will be useful to design new TACE inhibitors with improved potency.     

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.     

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.     

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     

Understanding the structure-activity and structure-selectivity correlation of cyclic guanine derivatives as phosphodiesterase-5 inhibitors by molecular docking, CoMFA and CoMSIA analyses

Molecular docking and 3D-QSAR analyses were performed to understand how PDE5 and PDE6 interact with a series of (49) cyclic guanine derivatives. Using the conformations of the compounds revealed by molecular docking, CoMFA and CoMSIA analyses resulted in the first quantitative structure-activity relationship (QSAR) and first quantitative structure-selectivity relationship (QSSR) models (with high cross-validated correlation coefficient q(2) and conventional correlation coefficient r(2) values) for predicting the inhibitory activity against PDE5 and the selectivity against PDE6. The high q(2) and r(2) values, along with further testing, indicate that the obtained 3D-QSAR and 3D-QSSR models will be valuable in predicting both the inhibitory activity and selectivity of cyclic guanine derivatives for these protein targets. A set of 3D contour plots drawn based on the 3D-QSAR and 3D-QSSR models reveal some useful clues to improve both the activity and selectivity by modifying structures of the compounds. It has been demonstrated that both the steric and electrostatic factors should appropriately be taken into account in future rational design and development of more active and more selective PDE5 inhibitors for the therapeutic treatment of erectile dysfunction (ED).     

3D-QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses using CoMFA and CoMSIA methods were conducted on a series of fluoropyrrolidine amides as dipeptidyl peptidase IV (DP-IV) inhibitors. The selected ligands were docked into the binding site of the 3D model of DP-IV using the GOLD software, and the possible interaction models between DP-IV and the inhibitors were obtained. Based on the binding conformations of these fluoropyrrolidine amides and their alignment inside the binding pocket of DP-IV, predictive 3D-QSAR models were established by CoMFA and CoMSIA analyses, which had conventional r ² and cross-validated coefficient values () up to 0.982 and 0.555 for CoMFA and 0.953 and 0.613 for CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were in the testing set. Structure-based investigations and the final 3D-QSAR results provide the guide for designing new potent inhibitors.