Synthesis,evaluation and CoMFA/CoMSIA study of nitrofuranyl methyl N-heterocycles as novel antitubercular agents

A series of novel nitrofuranyl methyl N-heterocycles based on the structure of IIIM-MCD-211 were designed and synthesized. Compounds 6d, 8b and 12a show excellent activity against MTB H37Rv strain (MIC: 0.031–0.062?μg/mL) roughly comparable to INH and IIIM-MCD-211. In addition, a three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on the above mentioned chemical series employing comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques. The developed CoMFA and CoMSIA models display high external predictability (r²_pred of 0.954 and 0.935, respectively) and good statistical robustness. More importantly, the newly designed compounds 16a and 16b (MIC: <0.016?μg/mL) based on the two models, as expected, were found to be more active than 12a and IIIM-MCD-21. Design and synthesis of more potent nitrofuranyl methyl N-heterocycles as anti-TB agents are currently in progress.     

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.     

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.     

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.     

3D-QSAR study of adamantyl N-benzylbenzamides as melanogenesis inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) of polyhydroxylated N-benzylbenzamide derivatives containing an adamantyl moiety were performed to understand the mechanism of action and structure–activity relationship of these compounds. Contour map analysis indicated that steric contributions of the adamantyl moiety and electrostatic contributions of the hydroxyl group at the 3-position are important in the activity. Activities of the training set and test sets predicted by CoMFA fit well with actual activities, demonstrating that CoMFA, along with the best calculated q² value, has the best predictive ability.     

Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents

Abstract

With the purpose of designing novel chemical entities with improved inhibitory potencies against drug-resistant Mycobacterium tuberculosis, the 3D- quantitative structure–activity relationship (QSAR) studies were carried out on biphenyl analogs of the tuberculosis (TB) drug, PA-824. Anti-mycobacterial activity (MABA) was considered for the 3D-QSAR studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The best CoMFA and CoMSIA models were found statistically significant with cross-validated coefficients (q²) of 0.784 and 0.768, respectively, and conventional coefficients (r²) of 0.823 and 0.981, respectively. The cross-validated and the external validation results revealed that both the CoMFA and CoMSIA models possesses high accommodating capacities and they would be reliable for predicting the pMIC values of new PA-824 derivatives. Based on the models and structural insights, a series of new PA-824 derivatives were designed and the anti-mycobacterial activities of the designed compounds were predicted based on the best 3D-QSAR model. The predicted data results suggest the designed compounds are more potent than existed ones.     

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.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

CoMFA based de novo design of Pyrrolidine Carboxamides as Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis

InhA, the enoyl acyl carrier protein reductase (EACP reductase) from Mycobacterium tuberculosis, is one of the key enzymes involved in the mycobacterial fatty acid elongation cycle and has been validated as an effective target for the development of anti-microbial agents. We report here, comparative molecular field analysis (CoMFA) studies and subsequent de novo ligand design using the LeapFrog program on pyrrolidine carboxamides, which have been reported as selective inhibitors of EACP reductase from Mycobacterium tuberculosis. The CoMFA model, constructed from the inhibitors used in this study has been successfully used to rationalize the structure-activity relationship of pyrrolidine carboxamides. The CoMFA model produced statistically significant results with cross-validated and conventional correlation coefficients of 0.626 and 0.953 respectively. Further, the predictive ability of CoMFA model was determined using a test set which gave predictive correlation coefficient r ² _pred of 0.880, indicating good predictive power. Finally, Leapfrog was used to propose 13 new pyrrolidine carboxamide analogues, based on the information derived from the CoMFA contour maps. The designed molecules showed better predicted activity using the CoMFA model with respect to the already reported systems; hence suggesting that newly proposed molecules in this series of compounds may be more potent and selective toward EACP reductase inhibition.     

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.     

Synthesis,structure analysis,antitumor evaluation and 3D-QSAR studies of 3,6-disubstituted-dihydro-1,2,4,5-tetrazine derivatives

3,6-Diaryl-dihydro-1,2,4,5-tetrazine derivatives were synthesized and their structures were confirmed by single-crystal X-ray diffraction. Monosubstituted dihydrotetrazines are the 1,4-dihydro structure, but disubstituted dihydrotetrazines are the 1,2-dihydro structure. The results of further research indicated there may be a rearrangement during the synthesis process of disubstituted dihydrotetrazines. Their antitumor activities were evaluated against A-549 and P388 cells in vitro. The results showed several compounds to be endowed with cytotoxicity in the low micromolar range. Two compounds were highly effective against A-549 cell and IC₅₀ values were 0.575 and 2.08 μM, respectively. Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 37 1,2,4,5-tetrazine derivatives with antitumor activity against A-549 cell. Models with good predictive abilities were generated with the cross validated q² values for CoMFA and CoMSIA being 0.744 and 0.757, respectively. Conventional r² values were 0.978 and 0.988, respectively, the predicted R² values were 0.916 and 0.898, respectively. The results provide the tool for guiding the design and synthesis of novel and more potent tetrazine derivatives.     

Molecular modelling studies on d-annulated benzazepinones as VEGF-R2 kinase inhibitors using docking and 3D-QSAR

Chemotypes comprising the d-annulated 1,3-dihydro-2H-1-benzazepin-2-one scaffold derived from the paullone structure were found to be potent vascular endothelial growth factor receptor 2 (VEGF-R2) kinase inhibitors. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies were performed on a series of d-annulated benzazepinones with VEGF-R2 kinase inhibition activities. The comparative molecular field analysis and comparative molecular similarity indices analysis models using 32 molecules in the training set gave r²_cv values of 0.811 and 0.769, r² values of 0.962 and 0.953, respectively. 3D contour maps generated from the two models revealed that the electron-withdrawing groups at R₁ and the bulky, electron-withdrawing as well as hydrogen bond donor groups at R₂ position are favourable; the bulky, hydrogen bond acceptor substituent at R₃ and the minor groups at R₄ position may benefit the potency. We have designed a series of novel VEGF-R2 inhibitors by utilizing the SAR results revealed in the present study, which were predicted with excellent potencies in the developed models. The results may aid in designing of potential VEGF-R2 inhibitors with better activities.     

3D-QSAR with CoMFA Model of Prolylendopeptidase Substrates

Abstract

The prolylendopeptidase (PEP) is the proteolytic enzyme, which plays an essential role in the regulation of some processes in central nervous system, such as memory, learning and behavior. It was shown that PEP activity changes at different diseases, like Parkinsons or Alzheimer's diseases, and some PEP inhibitors are used in therapy. At present time the discovery of new types of PEP inhibitors are the actual task.

In this study the structure of PEP active site was analyzed by 3D-QSAR with CoMFA methods using of 12 PEP substrates. The designed pharmacophore model assumes that substrates interact with PEP active site by pyrrolidol ring of proline residue and by hydrogen bonding.

The 3-D-QSAR + CoMFA model of PEP substrates propose that the hydrophobic bonds play the essential role in substrate interaction with enzyme. This model reveals the important steric and electrostatic areas around the molecules and the presence of substituents controls the PEP activity for substrates. Analysis of obtained data allows to assume, that substrate binding in PEP active site causes essential perturbations of substrate structure. This effect mainly depends on chemical nature of the amino acid side chain, located near to proline.     

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     

Exhaustive CoMFA and CoMSIA analyses around different chemical entities: a ligand-based study exploring the affinity and selectivity profiles of 5-HT1A ligands

The 5-hydroxytryptamine (5-HT_1A) receptors represent an attractive target in drug discovery. In particular, 5-HT_1A agonists and partial agonists are deeply investigated for their potential role in the treatment of anxiety, depression, ischaemic brain disorder and more recently, of pain. On the other hand, 5-HT_1A antagonists have been revealed promising compounds in cognition disorders and, lately, in cancer. Thus, the discovery of 5HT_1A ligands is nowadays an appealing research activity in medicinal chemistry. In this work, Comparative Molecular Fields Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied on an in-house library of 5-HT_1A ligands bearing different chemical scaffolds in order to elucidate their affinity and selectivity for the target_. Following this procedure, a number of structural modifications have been drawn for the development of much more effective 5-HT_1AR ligands.

     

3D-QSAR and docking studies of 3-arylquinazolinethione derivatives as selective estrogen receptor modulators

3D-QSAR and molecular docking analysis were performed to explore the interaction of estrogen receptors (ERα and ERβ) with a series of 3-arylquinazolinethione derivatives. Using the conformations of these compounds revealed by molecular docking, CoMFA analysis resulted in the first quantitative structure-activity relationship (QSAR) and first quantitative structure-selectivity relationship (QSSR) models predicting the inhibitory activity against ERβ and the selectivity against ERá. The q² and R² 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 3-arylquinazolinethione derivatives for these protein targets. A set of 3D contour plots drawn based on the 3D-QSAR and 3D-QSSR models reveal modifications of substituents at C2 and C5 of the quinazoline which my be useful to improve both the activity and selectivity of ERβ/ ERα. Results showed 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 ERβ inhibitors for the therapeutic treatment of osteoporosis. Figure Structures of ERβ binding with compounds 1aar, 1ax and 1aag obtained from molecular docking     

Improved 3D-QSAR CoMFA of the dopamine transporter blockers with multiple conformations using the genetic algorithm

A 3D-QSAR/CoMFA was performed for a series of 42 piperidine-based dopamine transporter (DAT) blockers. The overall process consisted of three major steps: (1) a pharmacophore model was built using the Genetic Algorithm Similarity Program (GASP); (2) the Flexible Superposition (FlexS) technique was applied to generate multiple conformations for each of the ligands based on the pharmacophore; (3) the Genetic Algorithm was employed to optimize the selection of the ligand conformations for the CoMFA modeling. The CoMFA models were found to be more detailed in the putative binding site by exploring multiple conformations of each ligand. The comparison of the contour maps shows that, in general, these models are comparable and the differences between them result from the ability of the flexible 3-substituents of the ligands to adopt multiple conformations satisfying the same pharmacophore model. These findings provide guidance for the design and improvement of compounds with DAT activity, which is important for the development of a treatment of cocaine addiction and certain neurological disorders.     

3D-QSAR and molecular modeling studies on 2,3-dideoxy hexenopyranosid-4-uloses as anti-tubercular agents targeting alpha-mannosidase

Ligand-based and structure-based methods were applied in combination to exploit the physicochemical properties of 2,3-dideoxy hex-2-enopyranosid-4-uloses against Mycobacterium tuberculosis H37Rv. Statistically valid 3D-QSAR models with good correlation and predictive power were obtained with CoMFA steric and electrostatic fields (r² = 0.797, q² = 0.589) and CoMSIA with combined steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (r² = 0.867, q² = 0.570) based on training set of 33 molecules with predictive r² of 0.808 and 0.890 for CoMFA and CoMSIA respectively. The results illustrate the requirement of optimal alkyl chain length at C-1 position and acceptor groups along hydroxy methyl substituent of C-6 to enhance the anti-tubercular activity of the 2,3-dideoxy hex-2-enopyranosid-4-uloses while any substitution at C-3 position exert diminishing effect on anti-tubercular activity of these enulosides. Further, homology modeling of M. tuberculosis alpha-mannosidase followed by molecular docking and molecular dynamics simulations on co-complexed models were performed to gain insight into the rationale for binding affinity of selected inhibitors with the target of interest. The comprehensive information obtained from this study will help to better understand the structural basis of biological activity of this class of molecules and guide further design of more potent analogues as anti-tubercular agents.