CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular 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 (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. 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. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

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.     

3D-QSAR studies on tripeptide aldehyde inhibitors of proteasome using CoMFA and CoMSIA methods

The ubiquitin-proteasome pathway plays a crucial role in the regulation of many physiological processes and in the development of a number of major human diseases, such as cancer, Alzheimer's, Parkinson's, diabetes, etc. As a new target, the study on the proteasome inhibitors has received much attention recently. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using comparative molecule field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) techniques were applied to analyze the binding affinity of a set of tripeptide aldehyde inhibitors of 20S proteasome. The optimal CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients (q(2)) of 0.615, 0.591 and conventional coefficients (r(2)) of 0.901, 0.894, respectively. These models were validated by a test set of eight molecules that were not included in the training set. The predicted correlation coefficients (r(2)) of CoMFA and CoMSIA are 0.944 and 0.861, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of beta5 subunit of 20S proteasome, which suggests that the 3D-QSAR models built in this paper can be used to guide the development of novel inhibitors of 20S proteasome.     

The 3D-QSAR study of antitumor arylsulfonylimidazolidinone derivatives by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of arylsulfonylimidazolidinone derivatives having antitumor activity were conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro cytotoxicity against human lung carcinoma (A549) exhibited a strong correlation with steric and electrostatic factors of the molecules. Four different types of models have been built using CoMFA and CoMSIA method with AM1 charge or Gasteiger-Huckel charge. By comparison of the statistical results of these models, model I obtained by CoMFA study with AM1 showed the best predictability of the antitumor activities based on the cross-validated value (0.642), conventional r2 (0.981), standard error of estimate (0.106) and PRESS value (0.170).     

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 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.     

CoMFA/CoMSIA 3D-QSAR of pyrimidine inhibitors of Pneumocystis carinii dihydrofolate reductase

Pneumocystis carinii is typically a non-pathogenic fungus found in the respiratory tract of healthy humans. However, it may cause P. carinii pneumonia (PCP) in people with immune deficiency, affecting mainly premature babies, cancer patients and transplant recipients, and people with acquired immunodeficiency syndrome (AIDS). In the latter group, PCP occurs in approximately 80% of patients, a major cause of death. Currently, there are many available therapies to treat PCP patients, including P. carinii dihydrofolate reductase (PcDHFR) inhibitors, such as trimetrexate (TMX), piritrexim (PTX), trimethoprim (TMP), and pyrimethamine (PMT). Nevertheless, the high percentage of adverse side effects and the limited therapeutic success of the current drug therapy justify the search for new drugs rationally planned against PCP. This work focuses on the study of pyrimidine inhibitors of PcDHFR, using both CoMFA and CoMSIA 3D-QSAR methods.     

Development of new CoMFA and CoMSIA 3D-QSAR models for anti-inflammatory phthalimide-containing TNFalpha modulators

In the present study, we describe a new 3D-QSAR analysis of 42 previously reported thalidomide analogues, with the ability to modulate the pro-inflammatory cytokine TNFalpha, by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Three statistically significant models were obtained. The best resulting CoMFA and CoMSIA models have conventional r(2) values of 0.996 and 0.983, respectively. The cross-validated q(2) values are 0.869 and 0.868, respectively. The analysis of CoMFA and CoMSIA contour maps provided insight into the possible sites for structural modification of the thalidomide analogues for better activity and reduced toxicity.     

3D-QSAR CoMFA/CoMSIA studies on Urokinase plasminogen activator (uPA) inhibitors: a strategic design in novel anticancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of indole/benzoimidazole-5-carboxamidines as urokinase plasminogen activator (uPA) inhibitors. The ligand molecular superimposition on template structure was performed by atom/shape-based RMS fit, multifit, and RMSD fit methods. The removal of two outliers from the initial training set of 30 molecules improved the predictivity of the models. The statistically significant model was established from 28 molecules, which were validated by evaluation of test set of nine compounds. The atom-based RMS alignment yielded best predictive CoMFA model (r2(cv) = 0.611, r2(cnv) = 0.778, F value = 43.825, r2(bs) = 0.842, r2(pred) = 0.616 with two components) while the CoMSIA model yielded (r2(cv) = 0.499, r2(cnv) = 0.976, F value=96.36, r2(bs) = 0.993, r2(pred) = 0.694 with eight components). The contour maps obtained from 3D-QSAR studies were appraised for the activity trends of the molecules analyzed. The results indicate that the steric, electrostatic, and hydrogen bond donor/acceptor substituents play significant role in uPA activity and selectivity of these compounds. The data generated from the present study can be used as putative pharmacophore in the design of novel, potent, and selective urokinase plasminogen activator inhibitors as cancer therapeutics.     

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.     

Synthesis and biological activities of benzofuran antifungal agents targeting fungal N-myristoyltransferase

The C-4 side chain modification of lead compound 1 has resulted in the identification of a potent and selective Candida albicans N-myristoyltransferase (CaNmt) inhibitor RO-09-4609, which exhibits antifungal activity against C. albicans in vitro. Further modification of its C-2 substituent has led to the discovery of RO-09-4879, which exhibits antifungal activity in vivo. The drug design is based on X-ray crystal analysis of a CaNmt complex with benzofuran derivative 4a. The optimization incorporates various biological investigations including a quasi in vivo assay and pharmacokinetic study. The computer aided drug design, synthesis, structure-activity relationships, and biological properties of RO-09-4879 are described in detail.     

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.     

Essential Structural Profile of Novel Adenosine Derivatives as Antiplatelet Aggregation Inhibitors Based on 3D-QSAR Analysis Using CoMFA,CoMSIA, and SOMFA

Russian Journal of Bioorganic Chemistry - —In this study, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and the self-organizing...     

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     

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.     

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.     

3D-QSAR CoMFA studies on bis-coumarine analogues as urease inhibitors: a strategic design in anti-urease agents

A 3D-QSAR study has been performed on thirty (30) bis-coumarine derivatives to correlate their chemical structures with their observed urease inhibitory activity. Due to the absence of information on their active mechanism, comparative molecular field analysis (CoMFA) was used in the study. Two different properties: steric, electrostatic, assumed to cover the major contributions to ligand binding, were used to generate the 3D-QSAR model. Significant cross-validated correlation coefficients q(2) (0.558) and r(2) (0.992) for CoMFA were obtained, indicating the statistical significance of this class of compounds. The red electrostatic contour map highlighting those portion of compounds which may be interacting with nickel metal center in the active site of urease; while the blue contour map indicates positively charged groups in the ligands have improved biological activity and thus lower the IC(50)s. The steric contour map shows that bulkier substitutions at the 'R' position are detrimental to ligand receptor interaction. Actual urease inhibitory activities of this class and the predicted values were in good agreement with the experimental results. Moreover, from the contour maps, the key features vital to ligand binding have been identified, which are important for us to trace the important properties and gain insight into the potential mechanisms of intermolecular interactions between the ligand and receptor.     

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.