Synthesis and biological validation of novel pyrazole derivatives with anticancer activity guided by 3D-QSAR analysis

Using literature data on anticancer activity of pyrazole derivatives, 3D-QSAR models were developed and 3D-QSAR analysis was performed. The 3D-QSAR analysis enabled identification of molecular properties that have the highest impact on antitumor activity against lung cancer cells. The results of 3D-QSAR analysis were taken into account while new compounds were designed. Obtained 3D-QSAR models were used for prediction of activity of new compounds. In this way, design of new compounds was guided by 3D-QSAR analysis which was performed on literature data. Ten new pyrazole derivatives were synthesised and their antitumor activities against A549 and NCIH23 lung cancer cells were validated. In order to obtain full profile of anticancer activity, cells viability (MTS) assays were combined with cell proliferation (BrdU) assays which measure actively dividing cells in treated sample. Experimental measurements showed good agreement between predicted and measured activities for majority of compounds. Also, anticancer activities of new pyrazole derivatives pointed to the chemical groups that can be useful in designing antitumor molecules. Substitution of hydrazine linker with rigid, 1,2,4-oxadiazole moiety resulted in compound 10, which has low (if any) cytotoxic activity and high potential cytostatic activity. Therefore, compound 10 presents a good starting point for design of new, more potent and safer anticancer therapeutics.     

Docking-based 3D-QSAR study for 11beta-HSD1 inhibitors

11beta-Hydroxysteroid dehydrogenase (11beta-HSD) enzymes catalyze the conversion of biologically inactive 11-ketosteroids into their active 11beta-hydroxy derivatives and vice versa. 11beta-HSD1 has been studied as a potential treatment for metabolic disease such as diabetes and obesity. To find correlation between 11beta-HSD1 and inhibitors, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on 70 inhibitors, based on molecular docking conformations obtained by using FlexX-Pharm. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Based on the docking results, highly predictive 3D-QSAR models were developed with q(2) values of 0.543 and 0.519 for CoMFA and CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. Therefore, these results should be useful to the prediction of the activities of new 11beta-HSD1 inhibitors.     

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.     

Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors

Three-dimensional quantitative structure-activity relationship (QSAR) studies were conducted on two classes of recently explored compounds with known YopH inhibitory activities. Docking studies were employed to position the inhibitors into the YopH active site to determine the probable binding conformation. Good correlations between the predicated binding free energies and the inhibitory activities were found for two subsets of phosphate mimetics: alpha-ketocarboxylic acid and squaric acid (R2=0.70 and 0.68, respectively). The docking results also provided a reliable conformational alignment scheme for 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed based on the docking conformations, giving q2 of 0.734 and 0.754 for CoMFA and CoMSIA models, respectively. The 3D-QSAR models were significantly improved after removal of an outlier (q2=0.829 for CoMFA and q2=0.837 for CoMSIA). The predictive ability of the models was validated using a set of compounds that were not included in the training set. Mapping the 3D-QSAR models to the active site of YopH provides new insight into the protein-inhibitor interactions for this enzyme. These results should be applicable to the prediction of the activities of new YopH inhibitors, as well as providing structural implications for designing potent and selective YopH inhibitors as antiplague agents.     

The discovery of inhibitors of Fas-mediated cell death pathway using the combined computational method

In this study, pharmacophore and 3D-QSAR models were developed for analogues of 3-substituted-benzofuran-2-carboxylate as inhibitors of Fas-mediated cell death pathways. Our pharmacophore model has good correspondence with experimental results and can explain the variance in biological activities coherently with respect to the structure of the data set compounds. The predictive power for our synthesized compounds were 0.96 for the pharmacophore model, 0.58 for the comparative molecular field analysis (CoMFA) model, and 0.57 for the comparative molecular similarity analysis (CoMSIA) model.     

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.     

Molecular modeling, design, synthesis, and biological evaluation of novel 3',4'-dicamphanoyl-(+)-cis-khellactone (DCK) analogs as potent anti-HIV agents

Our current studies aimed at developing new potential anti-AIDS drug candidates have focused on the design and synthesis of new DCK analogs with improved molecular water solubility. Based on the structures and biodata of previous DCK analogs, 3D-QSAR studies have been performed which resulted in two reliable computational models, CoMFA and CoMSIA, with r(2) values of 0.995 and 0.987, and q(2) values of 0.662 and 0.657, respectively. In accord with these 3D-QSAR models, 15 new DCK analogs with polar functional groups at the 3-position were subsequently designed, synthesized, and evaluated against HIV-1 replication in H9 and MT4 cell lines. New DCK analogs 3b, 3c, 4b, 4c, 6a, 7c, and 9a showed promising potency with EC(50) values ranging from 0.09 to 0.0002 microM in both assays. Meanwhile, these promising compounds also showed a wide range of predicted logP values from 0.90 to 5.19, which increased the probability of identifying anti-HIV drug candidates from this class of compounds for clinical trials. Furthermore, both experimental and predicted values matched well, corroborating the reliability of the established 3D-QSAR models.     

Structure-activity relationships of a series of tariquidar analogs as multidrug resistance modulators

Tariquidar (XR9576) analogs, modulators of cancer multidrug resistance (MDR), were subjected to QSAR and 3D-QSAR analyses. The structural features contributing to anti-MDR activity were identified by the Free-Wilson analysis and pharmacophore search using Hoechst 33342 as a template. 3D-QSAR CoMFA and CoMSIA models were derived and tested. The best models yielded an external predictivity of 0.66-0.75 squared correlation coefficient and outlined HB-acceptor, steric, and hydrophobic fields as the most important 3D properties. On the basis of the QSAR and 3D-QSAR analyses it was suggested that the strong inhibitory potency of the compounds studied is related to the presence of a bulky aromatic ring system with a 3rd positioned heteroatom toward the anthranilamide nucleus in the opposite end of the tetrahydroquinoline group. The results can help in directing the rational design of new generations of potent P-glycoprotein MDR modulators.     

Molecular docking and 3D-QSAR studies on the binding mechanism of statine-based peptidomimetics with beta-secretase

beta-Secretase is an important protease in the pathogenesis of Alzheimer's disease. Some statine-based peptidomimetics show inhibitory activities to the beta-secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on these analogues were performed. The Lamarckian Genetic Algorithm (LGA) was applied to locate the binding orientations and conformations of the peptidomimetics with the beta-secretase. A good correlation between the calculated binding free energies and the experimental inhibitory activities suggests that the identified binding conformations of these potential inhibitors are reliable. Based on the binding conformations, highly predictive 3D-QSAR models were developed with q(2) values of 0.582 and 0.622 for CoMFA and CoMSIA, respectively. The predictive abilities of these models were validated by some compounds that were not included in the training set. Furthermore, the 3D-QSAR models were mapped back to the binding site of the beta-secretase, to get a better understanding of vital interactions between the statine-based peptidomimetics and the protease. Both the CoMFA and the CoMSIA field distributions are in well agreement with the structural characteristics of the binding groove of the beta-secretase. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction would be useful in developing new drug leads against Alzheimer's disease.     

Inhibitory mode of indole-2-carboxamide derivatives against HLGPa: molecular docking and 3D-QSAR analyses

The interaction of a series of indole-2-carboxamide compounds with human liver glycogen phosphorylase a (HLGPa) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) of AutoDock 3.0 was employed to locate the binding orientations and conformations of the inhibitors interacting with HLGPa. The binding models were demonstrated in the aspects of inhibitor's conformation, subsite interaction, and hydrogen bonding. The very similar binding conformations of these inhibitors show that they interact with HLGPa in a very similar way. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. The structural and energetic differences in inhibitory potencies of indole-2-carboxamide compounds were reasonably explored. Using the binding conformations of indole-2-carboxamides, consistent and highly predictive 3D-QSAR models were developed by CoMFA and CoMSIA analyses. The q2 values are 0.697 and 0.622 for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four compounds that were not included in the training set. Mapping these models back to the topology of the active site of HLGPa leads to a better understanding of the vital indole-2-carboxamide-HLGPa interactions. Structure-based investigations and the final 3D-QSAR results provide clear guidelines and accurate activity predictions for novel inhibitor design.     

DAT/SERT selectivity of flexible GBR 12909 analogs modeled using 3D-QSAR methods

The dopamine reuptake inhibitor GBR 12909 (1-{2-[bis(4-fluorophenyl)methoxy]ethyl}-4-(3-phenylpropyl)piperazine, 1) and its analogs have been developed as tools to test the hypothesis that selective dopamine transporter (DAT) inhibitors will be useful therapeutics for cocaine addiction. This 3D-QSAR study focuses on the effect of substitutions in the phenylpropyl region of 1. CoMFA and CoMSIA techniques were used to determine a predictive and stable model for the DAT/serotonin transporter (SERT) selectivity (represented by pK(i) (DAT/SERT)) of a set of flexible analogs of 1, most of which have eight rotatable bonds. In the absence of a rigid analog to use as a 3D-QSAR template, six conformational families of analogs were constructed from six pairs of piperazine and piperidine template conformers identified by hierarchical clustering as representative molecular conformations. Three models stable to y-value scrambling were identified after a comprehensive CoMFA and CoMSIA survey with Region Focusing. Test set correlation validation led to an acceptable model, with q(2)=0.508, standard error of prediction=0.601, two components, r(2)=0.685, standard error of estimate=0.481, F value=39, percent steric contribution=65, and percent electrostatic contribution=35. A CoMFA contour map identified areas of the molecule that affect pK(i) (DAT/SERT). This work outlines a protocol for deriving a stable and predictive model of the biological activity of a set of very flexible molecules.     

Three-dimensional quantitative structure–activity relationship study on paullones as CDK inhibitors using CoMSIA and CoMFA

3D-QSAR studies were conducted on a series of paullones as CDK inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Two methods were compared: the widely used comparative molecular field analysis (CoMFA) and the recently reported comparative molecular similarity indices analysis (CoMSIA). Systematic variations of some parameters in CoMSIA and CoMFA were performed to search for the best 3D-QSAR model. The computed results showed that the 3D-QSAR models from CoMSIA were clearly superior to those from CoMFA. Using the best model from CoMSIA analysis, a significant cross-validated q² was obtained and the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The correlation results obtained from CoMSIA were graphically interpreted in terms of field contribution maps allowing physicochemical properties relevant for binding to be easily mapped back onto molecular structures. The features in the CoMSIA contour maps intuitively suggested where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity, which is very important for improving our understanding of the ligand-receptor interactions and in helping to design compounds with improved activity.     

Predicting the potency of hERG K<Superscript>+</Superscript> channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models

Blockade of the hERG K⁺ channel has been identified as the most important mechanism of QT interval prolongation and thus inducing cardiac risk. In this work, an ensemble of 3D-QSAR pharmacophore models was constructed to provide insight into the determinants of the interactions between the hERG K⁺ channel and channel inhibitors. To predict hERG inhibitory activities, the predicted values from the ensemble of models were averaged, and the results thus obtained showed that the predictive ability of the combined 3D-QSAR pharmacophore model was greater that those of the individual models. Also, using the same training and test sets, a 2D-QSAR model based on a heuristic machine-learning method was developed in order to analyze the physicochemical characters of hERG inhibitors. The models indicated that the inhibitors have certain key inhibitory features in common, including hydrophobicity, aromaticity, and flexibility. A final model was developed by combining the combined 3D-QSAR pharmacophore with the 2D-QSAR model, and this final model outperformed any other individual model, showing the highest predictive ability and the lowest deviation. This model can not only predict hERG inhibitory potency accurately, thus allowing fast cardiac safety evaluation, but it provides an effective tool for avoiding hERG inhibitory liability and thus enhanced cardiac risk in the design and optimization of new chemical entities.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

New potent 5-substituted benzofuroxans as inhibitors of Trypanosoma cruzi growth: quantitative structure-activity relationship studies

Benzofuroxan derivatives have been shown to inhibit the growth of Trypanosoma cruzi, the etiological agent of Chagas' disease. Therefore, 2D- and 3D-QSAR models of their in vitro antichagasic activity were developed. Six new derivatives were synthesized to complete a final set of 26 structurally diverse benzofuroxans. The 2D-QSAR model (r = 0.939, r(adj)(2) = 0.849) was generated using multiple regression analysis of tabulated substituents' physicochemical properties and indicator variables. In addition, a 3D-QSAR model (r(2) = 0.997, q(2) = 0.802) was obtained using a comparative molecular field analysis (CoMFA). Due to the well-known benzofuroxan tautomerism, in both approaches (2D- and 3D-QSAR) it was necessary to include an indicator variable to consider the N-oxide position (I(6)). This parameter was established using low-temperature NMR experiments. Both QSAR models identified the electrophilic character of the substituent alpha-atom as a requirement for activity. Further support was found using a density functional theory (DFT) approach.     

3D-QSAR study of Chk1 kinase inhibitors based on docking

Checkpoint kinase 1 (Chk1), a kind of a serine/threonine protein kinase, plays a significant role in DNA damage-induced checkpoints. Chk1 inhibitors have been demonstrated to abrogate the S and G2 checkpoints and disrupt the DNA repair process, which results in immature mitotic progression, mitotic catastrophe, and cell death. Normal cells remain at the G1 phase via p53 to repair their DNA damages, and are less influenced by the abrogation of S and G2 checkpoint. Therefore, selective inhibitors of Chk1 may be of great therapeutic value in cancer treatment. In this paper, in order to understand the structure-activity relationship of macro-cyclic urea Chk1 inhibitors, a study combined molecular docking and 3D-QSAR modeling was carried out, which resulted in two substructure-based 3D-QSAR models, including the CoMFA model (r(2), 0.873; q(2), 0.572) and CoMSIA model (r(2), 0.897; q(2), 0.599). The detailed microscopic structures of Chk1 binding with inhibitors were performed by molecular docking. Two docking based 3D-QSAR models were developed (CoMFA with r(2), 0.887; q(2), 0.501; CoMSIA with r(2), 0.872; q(2), 0.520). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures would be helpful to better understand the structure-activity relationship. All the conclusions drawn from both the 3D-QSAR contour maps and molecular docking were in accordance with the experimental activity dates. The results suggested that the developed models and the obtained CHk1 inhibitor binding structures might be reliable to predict the activity of new inhibitors and reasonable for the future drug design.     

Predictive screening model for potential vector-mediated transport of cationic substrates at the blood–brain barrier choline transporter

A set of semi-rigid cyclic and acyclic bis-quaternary ammonium analogs, which were part of a drug discovery program aimed at identifying antagonists at neuronal nicotinic acetylcholine receptors, were investigated to determine structural requirements for affinity at the blood–brain barrier choline transporter (BBB CHT). This transporter may have utility as a drug delivery vector for cationic molecules to access the central nervous system. In the current study, a virtual screening model was developed to aid in rational drug design/ADME of cationic nicotinic antagonists as BBB CHT ligands. Four 3D-QSAR comparative molecular field analysis (CoMFA) models were built which could predict the BBB CHT affinity for a test set with an r² <0.5 and cross-validated q² of 0.60, suggesting good predictive capability for these models. These models will allow the rapid in silico screening of binding affinity at the BBB CHT of both known nicotinic receptor antagonists and virtual compound libraries with the goal of informing the design of brain bioavailable quaternary ammonium analogs that are high affinity selective nicotinic receptor antagonists.     

3D-QSAR and molecular docking studies of benzaldehyde thiosemicarbazone, benzaldehyde, benzoic acid, and their derivatives as phenoloxidase inhibitors

Phenoloxidase (PO), also known as tyrosinase, is a key enzyme in insect development, responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. Inhibition of PO may provide a basis for novel environmentally friendly insecticides. In the present study, we determined the inhibitory activities and IC50 values of 57 compounds belonging to the benzaldehyde thiosemicarbazone, benzaldehyde, and benzoic acid families against phenoloxidase from Pieris rapae (Lepidoptera) larvae. In addition, the inhibitory kinetics of 4-butylbenzaldehyde thiosemicarbazone against PO was measured in air-saturated solutions for the oxidation of L-3,4-dihydroxyphenylalanine (L-DOPA). The results indicated that the compound is a reversible noncompetitive inhibitor. The bioactivity results were used to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models using two molecular field analysis techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). After carrying out superimposition using common substructure-based alignment, robust and predictive 3D-QSAR models were obtained from CoMFA (q2=0.926, r2=0.986) and CoMSIA (q2=0.933, r2=0.984) with six optimum components. The 3D-QSAR model built here will provide hints for the design of novel PO inhibitors. The molecular interactions between the ligands and the target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the representative compound 4-butylbenzaldehyde thiosemicarbazone and the active site was elucidated in detail.