3D QSAR studies on 5-(2-methylbenzimidazol-1-yl)-N-alkylthiophene-2-carboxamide derivatives as P. falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors

Malaria is currently one of the world’s most severe endemic diseases, responsible for majority of morbidity and mortality. A large number of drugs are available for its treatment; however, the development of resistance has become more widespread with most of the frontline drug therapies. Inhibitors of PfDHODH have proven efficacy for the treatment of malaria. 3D QSAR studies on some 5-(2-methylbenzimidazol-1-yl)-N-alkylthiophene-2-carboxamide derivatives as PfDHODH inhibitors were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The alignment strategy was used for these compounds by means of Distill function defined in SYBYL x 1.2. The best CoMFA and CoMSIA models obtained for the training set were statistically significant with q ² of 0.669 and 0.727, cross-validated coefficient (r ² _cv) of 0.603 and 0.698, and conventional coefficients (r ²) of 0.971 and 0.966, respectively. Both the models were validated by an external test set of five compounds giving satisfactory prediction (r ² _pred) of 0.799 and 0.815 for CoMFA and CoMSIA models, respectively. Further the robustness of the model was verified by bootstrapping analysis. Generated CoMFA and CoMSIA models provide useful information for the design of novel inhibitors with better PfDHODH inhibitory activity.     

3D QSAR study of 4H-chromen-1,2,3,4-tetrahydropyrimidine-5-carboxylate derivatives as potential anti-mycobacterial agents

In this study, 3D QSAR (CoMFA and CoMSIA) analysis was performed on 4H-chromen-1,2,3,4-tetrahydropyrimidine-5-carboxylate derivatives as potential anti-mycobacterial agents. ‘Distill’ function in SYBYL X 1.2 was used for alignment of the molecules. The best CoMFA and CoMSIA models were obtained for the training set compounds with leave-one-out correlation coefficients (q ²) of 0.753 and 0.646, cross validated correlation coefficients (r _cv ² ) of 0.714 and 0.619, and conventional coefficients (r ²) of 0.975 and 0.983, respectively. Both the models were validated by a test set of 8 compounds giving satisfactory prediction (r _pred ² ) of 0.788 and 0.663 for CoMFA and CoMSIA models, respectively. The results of the study would provide useful information for the design of new compounds and it would also help in prediction of activity of designed compounds prior to their synthesis.     

Cytotoxicity and Structure-activity Relationships of Naphthyridine Derivatives in Human Cervical Cancer,Leukemia, and Prostate Cancer

Naphthyridine compounds are important, because they exhibit various biological activities including anticancer, antimicrobial, and anti-inflammatory activity. Some naphthyridines have antimitotic effects or demonstrate anticancer activity by inhibiting topoisomerase II. These compounds have been investigated as potential anticancer agents, and several compounds are now part of clinical trials. A series of naphthyridine derivatives were evaluated for their in vitro cytotoxic activities against human cervical cancer (HeLa), leukemia (HL-60), and prostate cancer (PC-3) cell lines using an MTT assay. Some compounds (14, 15, and 16) were more potent than colchicine against all three human cancer cell lines and compound (16) demonstrated potency with IC₅₀ values of 0.7, 0.1, and 5.1 µM, respectively. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used for quantitative structure-activity relationship (QSAR) molecular modeling of these compounds. We obtained accurate and predictive three-dimensional QSAR (3D-QSAR) models as indicated by the high PLS parameters of the HeLa (q², 0.857; r², 0.984; r²_pred, 0.966), HL-60 (q², 0.777; r², 0.937; r²_pred, 0.913), and PC-3 (q², 0.702; r², 0.983; r²_pred, 0.974) cell lines. The 3D-QSAR contour maps suggested that the C-1 NH and C-4 carbonyl group of the naphthyridine ring and the C-2 naphthyl ring were important for cytotoxicity in all three human cancer cell lines.     

STUDIES ON NOVEL NON-IMIDAZOLE HUMAN H4 RECEPTOR ANTAGONISTS USING GFA AND FREE-WILSON ANALYSIS

The Histamine H4 receptor is expressed mainly on eosinophils and mast cells and has been shown to be involved in the chemotaxis of both the cell types. The receptor is also implicated in the release of IL-16, from CD8⁺ T cells. This data indicate that the receptor may play a role in inflammatory process. Herein we describe 3D QSAR studies of non-imidazole Indolylpiperazines using GFA and Free Wilson Analysis. For the GRA model, thirteen molecules were used as training set and three as test set to evaluate the external predictability of the equations generated using GFA [r² = 0.650 and r²_pred = 0.807]. The results indicate that electronic and thermodynamic parameters are major contributors to the activity. The same set of molecules was analyzed using the Free-Wilson analysis and a good correlation was obtained [r² = 0.988].     

CoMFA AND CoMSIA STUDIES ON A SET OF BENZYL PIPERAZINES, PIPERADINES, PYRAZINOPYRIDOINDOLES, PYRAZINOISOQUINOLINES AND SEMI RIGID ANALOGS OF DIPHENHYDRAMINE

3D QSAR studies using CoMFA and CoMSIA have been carried out using 40 antihistamines H1 belonging to substituted benzyl piperazines/piperidines, pyrazinopyridoindoles, pyrazinoisoquinolines and semirigid analogs of diphenhydramine. Chemical feature based pharmacophoric alignments were used to develop models using different fields viz. tripos standard, hydrogen bonding, parabolic indicator in the case of CoMFA and steric, electrostatic, donor, acceptor, hydrophobic, donor and acceptor, steric and electrostatic in the case of CoMSIA. All the eleven derived models were further optimized using the PLS region focusing tool to increase the model resolution which led to eleven more models. The removal of two outliers led to statistically significant models. The best CoMFA models was the one developed using the (region focused) CoMFA parabolic fields (r²=0.988,q² _LOO = 0.596, q² _{Leave 10% out} =0.592, q² _{Leave 30% out} = 0.606) and the best CoMSIA model was the one with (region focused) CoMSIA steric fields (r2=0.989, q² _LOO =0.74, q² _{Leave 10% out} = 0.726, q² _{Leave 30% out} =0.74).     

Molecular modeling studies on 3,4-dihydroquinazolines as trypanothione reductase inhibitors using 3D-QSAR and docking approaches

The trypanothione reductase (TryR) has been used as a key validated target to guide drug discovery for human African trypanosomiasis (HAT). 3D-QSAR and docking studies were performed on a series of 3,4-dihydroquinazolines as TryR inhibitors to establish a molecular model for new drug design. The CoMFA and CoMSIA models resulted from 53 molecules gave r _cv ² values of 0.591 and 0.574, r ² values of 0.968 and 0.943, respectively. The external validation indicated that CoMSIA model with a valid r _m ² value of 0.864 exhibited better predictive power than CoMFA model. 3D contour maps generated from CoMFA and CoMSIA along with the docking analyses have identified several key features responsible for the activity. A set of analogs were proposed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The results can be served as a useful guideline for designing novel 3,4-dihydroquinazoline derivatives with improved activity against human African trypanosomes.     

CoMFA and CoMSIA analysis of protein kinase B (PKBβ) inhibitors using various alignment methods

Protein kinase B (PKB) is considered as a key mediator of proliferation and survival pathways, which involved in the development of several human cancers. PKB is a recognized target for the development of small-molecule inhibitors for the treatment of cancer. In this study a diverse set of 73 PKBβ inhibitors were aligned by three different methods (pharmacophore, docking-based, and rigid body alignment) for CoMFA and CoMSIA analysis. The best 3D QSAR models were obtained using pharmacophore-based alignment. CoMFA and CoMSIA models were found statistically significant with leave-one-out correlation coefficients (q ²) of 0.613 and 0.562 respectively, cross-validated coefficients (r ² _cv ) of 0.609 and 0.558, respectively and conventional coefficients (r ²) of 0.914 and 0.989, respectively. 3D QSAR models were validated by a test set of 12 compounds giving satisfactory predicted correlation coefficients (r ² _pred ) of 0.767 and 0.622 for CoMFA and CoMSIA models, respectively. This study provides valuable clues to design new compounds against PKBβ.     

3D-QSAR and docking studies on pyridopyrazinones as BRAF inhibitors

BRAF has become an important and exciting therapeutic target toward human cancer. 3D-QSAR and docking studies were performed to explore the interaction of the BRAF with a series of pyridopyrazinones. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were carried out in terms of their potential for predictability. The CoMFA and CoMSIA models using 71 compounds in the training set gave r _cv² values of 0.567 and 0.662, r ² values of 0.900 and 0.907, respectively. The 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. Molecular docking was applied to explore the binding mode between the ligands and the receptor. The information obtained by 3D-QSAR models may be useful to design novel potential BRAF inhibitors.     

Three-dimensional QSAR and pharmacophore mapping of biphenyl benzoic acid derivatives as selective human β3-adrenergic receptor agonists

Molecular modeling studies were performed to develop a predictive common pharmacophore hypothesis (CPH) and use it for alignment in three-dimensional (3D) quantitative structure-activity relationship (QSAR) studies using CoMFA and CoMSIA, with a diverse set of 80 β₃-adrenergic receptor (β₃-AR) agonists. Using PHASE (Pharmacophore Alignment and Scoring Engine) six-point CPH with one acceptor, one negative charge, one positive charge, and three rings, features were derived for pharmacophore-based alignment of molecules. CPH was selected by correlating the observed and estimated activity for the training set and test set of molecules using partial least squares analysis. The validated pharmacophore hypothesis was used for alignment of molecules in CoMFA and CoMSIA model development. The models so generated showed a good “predictive” r ² value of 0.6635 and 0.8665 for CoMFA and CoMSIA, respectively. The 3D contour CoMFA/CoMSIA maps provided an interpretable explanation of SAR for the compounds and also permitted interesting conclusions about the substituent effects at different positions of the biphenyl benzoic acid derivatives. CPH can also provide a powerful template for virtual screening and design of new β₃-AR agonists.     

Comparative molecular field analysis (CoMFA) of a series of selective adenosine receptor A2A antagonists

Selective and potent antagonists for the A_2A adenosine receptors have been described recently. The most potent compounds have a triazolo‐pyrimidine structure, whereas 8‐styryl‐xanthines usually possess lower affinity at the A_2A receptor. We have examined the quantitative structure activity relationships of 34 triazolo‐pyrimidines using the Comparative Molecular Field Analysis (CoMFA). The model developed accounts in a satisfactory manner for the structure‐activity relationships within this series of A_2A receptor antagonists (q² = 0.840, r² = 0.970) and, consequently, it can be used as a guide in the design of novel analogs with optimized antagonistic activity. The overall predictivity of this model was tested on the published data of a set of external molecules giving acceptable results. The validity of the CoMFA approach as an effective tool for studying the 3D‐QSAR of series of biologically active compounds is confirmed, even if additional efforts clearly are needed for trying to extend the models to structurally more varied series of derivatives. Drug Dev. Res. 46:126–133, 1999. © 1999 Wiley‐Liss, Inc.     

CoMFA on the melanogenesis inhibitory activity of alkyl-3,4-dihydroxybenzoate, N-alkyl-3,4-dihydroxybenzamide analogues, and prediction of higher active compounds

To predict a new materials of superior melanogenesis inhibitory activities (MIA), the comparative molecular field analysis (CoMFA) models on MIA of alkyl-3,4-dihydroxybenzoates and N-alkyl-3,4-dihydroxybenzamides analogues against mouse melanoma cell were derived and discussed quantitatively. The optimized CoMFA model II from the field fit alignment demonstrated better predictability of molecular structure with the non-cross validated conventional coefficient (r² _nev.=0.984) and cross-validated coefficient (r² _cv. or q=0.706) than that from atom based fit alignment. Also, the relative contribution of the optimized CoMFA model II showed the steric (63.8%), electrostatic (18.4%), and hydrophobic (ClogP) field (17.8%), respectively. The results indicated that the esters (alkyl-3,4-dihydroxybenzoates) are more active inhibitors than the amides (N-alkyl-3,4-dihydroxybenzamides). Furthermore, the optimized CoMFA model II is proven to be a useful approach to design a highly active melanogenesis inhibitor molecules, and enables to predict R₁=n-dodecy and R₂=n-heptyloxy substituted compound of alkyl-3,4-dihydroxybenzoates as the most active compounds (Pred. pI₅₀=5.87).     

3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors

The present work was focused on the study of the three-dimensional (3D) structural requirements for the highly potent bioactivity of dipeptidyl peptidase (DPP) IV's inhibitor. At first, molecular dynamic and mechanic (MD/MM) simulations were performed to research the conformations of the potent DPP IV's inhibitor 5-(aminomethyl)-6-(2,4-dichlorophenyl)-2-(3,5-dimethoxy-phenyl)pyrimidin-4-amine. Using the MD/MM-determined molecular conformers as templates, the 3D quantitative structure activity relationship (QSAR) studies were carried out based on a set of arylmethylamine DPP IV inhibitors with the comparative molecular field analysis (CoMFA) approach. The best 3D-QSAR model was constructed with good statistic values of r_cv² and R² using PLS analyses (CoMFA: r_cv²=0.660, R²=0.953). The generated 3D-QSAR model was proved to be reliable by internal and external validations. Docking studies were further performed to analyze the interaction mode between the highly potent or low potent arylmethylamine derivatives and DPP IV. Our flexible docking results also confirmed the possible bioactive conformation obtained from the 3D-QSAR model, of arylmethylamine-based DPP IV inhibitors. The 3D-QSAR model may provide information of pharmacophoric features for further design and optimization of new scaffold compounds with high inhibitory activity to DPP IV.     

3-甲基芬太尼衍生物立体异构体的 QSAR 研究

用比较分子力场分析(CoMFA)方法研究了3-甲基芬太尼和羟甲芬太尼立体异构体的三维定量构效关系(3D-QSAR)。所得CoMFA-QSAR模型有很好的预测能力(γ²_{cros-validated}=0.716,n_{optimalcomponent}=5,γ²_conventional=0.999,s=0.052,F=1305.1),模型中,被研究化合物的构象可能就是其活性构象。以AM1方法进行量子化学计算,获得上述可能活性构象的结构参数及空间位置参数。基于这些参数,用偏最小二乘法(PLS)获得了被研究化合物的QSAR方程。所得PLS-QSAR模型具有较好的预测能力,并且显示被研究化合物的镇痛活性取决于分子中负电性的哌啶氮原子(N_PA)净电荷以及哌啶氮原子、羰基氧原子、1-β-苯环、4-N-苯环、3-甲基和2′-羟基的空间位置。     

Precise prediction of activators for the human constitutive androstane receptor using structure-based three-dimensional quantitative structure–activity relationship methods

The constitutive androstane receptor (CAR, NR1I3) regulates the expression of numerous drug-metabolizing enzymes and transporters. The upregulation of various enzymes, including CYP2B6, by CAR activators is a critical problem leading to clinically severe drug–drug interactions (DDIs). To date, however, few effective computational approaches for identifying CAR activators exist. In this study, we aimed to develop three-dimensional quantitative structure–activity relationship (3D-QSAR) models to predict the CAR activating potency of compounds emerging in the drug-discovery process. Models were constructed using comparative molecular field analysis (CoMFA) based on the molecular alignments of ligands binding to CAR, which were obtained from ensemble ligand-docking using 28 compounds as a training set. The CoMFA model, modified by adding a lipophilic parameter with calculated logD_7.4 (S+logD_7.4), demonstrated statistically good predictive ability (r² = 0.99, q² = 0.74). We also confirmed the excellent predictability of the 3D-QSAR model for CAR activation (r²_pred = 0.71) using seven compounds as a test set for external validation. Collectively, our results indicate that the 3D-QSAR model developed in this study provides precise prediction of CAR activating potency and, thus, should be useful for selecting drug candidates with minimized DDI risk related to enzyme-induction in the early drug-discovery stage.     

Predictive 3D-QSAR and HQSAR model generation of isocitrate lyase (ICL) inhibitors by various alignment methods combined with docking study

Isocitrate lyase (ICL) is one of the most important targets in the treatment of Mycobacterium tuberculosis. In this study a diverse set of 2-benzanilide derivatives were aligned by two different methods for CoMFA, CoMSIA, and HQSAR analysis. The best CoMFA model was obtained with the internal validation value (q ²) of 0.730 and conventional coefficient (r ²) of 0.944. Various CoMSIA models were generated and cross-validated. The best cross-validation coefficient (q ²) value was found to be statistically satisfactory (0.688). Both the models were validated by test set of 10 compounds with satisfactory prediction value of (r ² _pred ) 0.725 and 0.631 for CoMFA and CoMSIA, respectively. Cross-validation coefficient value (q ²) of 0.694 and r ² of 0.856 were obtained for HQSAR study. The docking study reveals that large hydrophobic pockets occupy R substitutions of these compounds. An electronically negative surface is observed near R₁ substitution. The results of the 3D-QSAR analysis corroborate with the molecular docking results, and our findings will serve as a basis for further development of better allosteric inhibitors of ICL inhibitors against M. tuberculosis.