Abstract: A series of simple heterocyclic chalcone analogues have been synthesized by Claisen Schmidt condensation reactions between substituted benzaldehydes and heteroaryl methyl ketones and evaluated for their antibacterial activity. The structures of the synthesized chalcones were established by IR and <sup>1</sup>H-NMR analysis. The biological data shows that compounds <strong>p<sub>5</sub></strong>, <strong>f<sub>6</sub></strong> and <strong>t<sub>5</sub></strong> had strong activities against both susceptible and resistant <em>Staphylococcus aureus </em>strains, but not activity against a vancomycin and methicillin resistant <em>Staphylococcus aureus</em> isolated from a human sample. The structure and activity relationships confirmed that compounds <strong>f<sub>5</sub></strong>, <strong>f<sub>6</sub></strong> and <strong>t<sub>5</sub></strong> are potential candidates for future drug discovery and development.
Abstract: Benzo[<em>c</em>]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, hologram-QSAR, 2D-QSAR and 3D-QSAR models were developed for BCPs on topoisomerase I inbibitory activity and cytotoxicity against seven tumor cell lines including RPMI8402, CPT-K5, P388, CPT45, KB3-1, KBV-1and KBH5.0. The hologram, 2D, and 3D-QSAR models were obtained with the square of correlation coefficient R<sup>2</sup> = 0.58 - 0.77, the square of the crossvalidation coefficient q<sup>2</sup> = 0.41 - 0.60 as well as the external set's square of predictive correlation coefficient r<sup>2</sup> = 0.51 - 0.80. Moreover, the assessment method based on reliability test with confidence level of 95% was used to validate the predictive power of QSAR models and to prevent over-fitting phenomenon of classical QSAR models. Our QSAR model could be applied to design new analogues of BCPs with higher antitumor and topoisomerase I inhibitory activity.
Abstract: Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors identified from a large set via a random forest algorithm. Overall accuracy of up to 87% and a Matthews coefficient correlation (MCC) of 0.71 were obtained after this SVM classifier was validated internally by a test set of 15 compounds. For two external test sets, 89% and 80% BCP compounds, respectively, were correctly predicted. The results indicated that our SVM model could be used as the filter for designing new BCP compounds with higher TOP-I inhibitory activity.
Abstract: A total of 30 chalcone analogues was synthesized via a base catalyzed Claisen Schmidt condensation and screened for their in vitro antibacterial activity against Methicillin-sensitive Staphylococcus aureus (MSSA) and Methicillin-resistant Staphylococcus aureus (MRSA) alone or in combination with non beta-lactam antibiotics namely ciprofloxacin, chloramphenicol, erythromycin, vancomycin, doxycycline and gentamicin. In the checkerboard technique, fractional inhibitory concentration indices (FICI) show that the following combinations like ciprofloxacin with 25 (4'-bromo-2-hydroxychalcone); doxycycline with 21 (4-hydroxychalcone); doxycycline with 25; and doxycycline with 4 (2',2-dihydroxychalcone) were synergistic against MRSA. In term SAR study, the relationship between chalcone structure and their antibacterial activity against S. aureus and synergy with tested antibiotics were discussed. Possible mechanisms for antibacterial activity of chalcones alone as well as the synergistic effect in combinations were proposed by molecular modeling studies, respectively. Combinations of chalcones with conventional antibiotics could be an effective alternative in the treatment of infection caused by MRSA.
Abstract: The inner cavity of the hERG potassium ion channel can accommodate large, structurally diverse compounds that can be trapped in the channel by closure of the activation gate. A small set of propafenone derivatives was synthesized, and both use-dependency and recovery from block were tested in order to gain insight into the behavior of these compounds with respect to trapping and non-trapping. Ligand-protein docking into homology models of the closed and open state of the hERG channel provides the first evidence for the molecular basis of drug trapping.
Abstract: There is an increasing interest in computational models for the classification and prediction of the human ether-a-go-go-related-gene (hERG) potassium channel affinity in the early phase of drug discovery and development. In this study, similarity-based SIBAR descriptors were applied in order to develop and validate in silico binary QSAR and counter-propagation neural network models for the classification of hERG activity. The SIBAR descriptors were calculated based on four reference datasets using four sets of 2D- and 3D-descriptors including 3D-grid-based VolSurf, 3D 'inductive' QSAR, Van der Waals surface area (P_VSA) and a set of 11 hERG relevant 2D descriptors devised from feature selection methods. The results indicate that the reference data set tailored to the specific problem, together with a set of hERG relevant descriptors, provides highly predictive models.
Abstract: A series of 2'-hydroxychalcones has been synthesized and screened for their in vitro inhibitory activities of cyclooxygenase-2 catalyzed prostaglandin production from lipopolysaccharide-treated RAW 264.7 cells. Structure-activity relationship study suggested that inhibitory activity against prostaglandin E(2) production was governed to a greater extent by the substituent on B ring of the chalcone, and most of the active compounds have at least two methoxy or benzyloxy groups on B ring. The relationship between chalcone structures and their PGE(2) inhibitory activities was also interpreted by docking study on cyclooxygenase-2.
Abstract: Since the advent of QSAR (quantitative structure activity relationship) modeling quantitative representations of molecular structures are encoded in terms of information-preserving descriptor values. Nowadays, a nearly infinite variety of potential descriptors is available and descriptor selection is no longer a task which can be done manually. There is an increasing need for automation in order to reduce the dimensionality of the descriptor space. Classical feature selection (FS) and dimensionality reduction (DR) methods like principal component analysis, which relies on the selection of those descriptors that contribute most to the variance of a data set, often fail in providing the best classification result. More sophisticated methods like genetic algorithms, self-organizing-maps and stepwise linear discriminant analysis have proven to be useful techniques in the process of selecting descriptors with a significant discriminative power.
The topic FS and DR becomes even more important when predictive models are approached which should describe the QSAR of highly promiscuous target proteins. The ABC-transporter family, the cardiac hERG-potassium channel, and the hepatic cytochrom-P450-family are classical representatives of such poly-specific proteins. In this case the interaction pattern is a rather complex one and thus the selection of the most predictive descriptors needs advanced methods. This review surveys FS and DR methods that have recently been successfully applied to classify ligands of poly-specific target proteins.
Abstract: Counter-propagation neural networks were used to develop computational models for classification and prediction of human ether-a-go-go-related-gene (hERG) potassium channel blockers. The data set used includes 285 compounds taken from literature sources and two sets of 2D molecular descriptors, one is based on 32 P_VSA descriptors derived from moe and the other comprises 11 descriptors retrieved by a feature selection method. The counter-propagation neural networks with a 3-dimensional output layer combined with a set of 11 hERG relevant descriptors showed best performance, especially in classifying compounds in the middle-activity class (hERG IC(50) = 1-10 microm). The total accuracy values obtained for training and test sets are 0.93-0.95 and 0.83-0.85, respectively. In each activity class (low, medium, high), 'Goodness of Hit lists' GH scores archived range from 0.89 to 0.97 for the training set and from 0.74 to 0.87 for the test set. This model thus provides possible strategies for improving the performance of predicting and classifying compounds having hERG IC(50) in the range of 1-10 microm.
Abstract: Acquired long QT syndrome causes severe cardiac side effects and represents a major problem in clinical studies of drug candidates. One of the reasons for development of arrhythmias related to long QT is inhibition of the human ether-a-go-go-related-gene (hERG) potassium channel. Therefore, early prediction of hERG K(+) channel affinity of drug candidates is becoming increasingly important in the drug discovery process. Binary QSAR models with threshold values at IC(50)=1 and of 10 microM, respectively, were generated using two different sets of descriptors. One set comprising 32 P_VSA descriptors and the other one utilizing a set of descriptors identified out of a large set via a feature selection algorithm. For the full dataset, the power for classification of hERG blockers was 82-88%, which meets prior classification models. Considering the fact that 2D descriptors are fast and easy to calculate, these binary QSAR models are versatile tools for use in virtual screening protocols.
Abstract: Acquired long QT syndrome caused by drugs that block the human ether-a-go-go-related-gene (hERG) K(+) channel causes severe side effects and thus represents a major problem in clinical studies of drug candidates. Therefore, early prediction of hERG K(+) channel affinity of drug candidates is becoming increasingly important in the drug discovery process. Both structure-based and ligand-based approaches have been undertaken to shed more light on the molecular basis of drug-channel interaction. In this article, in silico approaches for prediction of interaction with hERG are reviewed. Special attention is drawn to the in vitro biological testing systems as well as to consensus approaches for improvement of predictive power.
Abstract: Sildenafil, one of selective phosphodiesterase 5 (PDE5) inhibitors, is a widely used oral agent for the treatment of erectile dysfunction. To develop new PDE5 inhibitors with improved therapeutic efficacy, a series of sildenafil analogues have been prepared and their in vitro PDE5 inhibitory activities were evaluated. Their IC(50) values ranged from 423 to 0.05 nM. Herein, the results of 3D-QSAR (CoMFA and CoMSIA) analyses on these inhibitors are reported. Both CoMFA and CoMSIA gave reliable models with q(2) values >0.75 and r(2) values >0.99. The resulting CoMFA and CoMSIA models reveal a good correlation between the contour maps and the active site residues critical for the interaction with inhibitor, and nicely predict the key structural features of new analogues with improved activity and selectivity.
Abstract: We describe the first discovery of small molecules that bind to the Z-DNA binding domain of human ADAR1 (Adenosine Deaminase Acting on RNA 1) by structure-based virtual screening of chemical database. These molecules bind to Z-DNA binding domain to inhibit the interaction with the Z-DNA. Many viruses have Z-DNA binding proteins, which are structurally similar to Z-DNA binding domain of human ADAR1, and the ability of Z-DNA binding protein to bind the Z-DNA is essential for their pathogenicity. Therefore, the molecules identified in this study may serve as novel leads for the design of agents that inhibit biological functions of those pathogenic viruses.
Abstract: Synthesis of new sildenafil analogues containing a phosphonate group in the 5(')-sulfonamide moiety of the phenyl ring, 12a-e, 13a-d, and 14a-d, and evaluation of their in vitro PDE5 inhibitory activity are disclosed. Enzyme assays revealed that maximum 10-fold increase in PDE5 inhibitory activity, compared with sildenafil, was achieved by introducing a phosphonate group in the 5(')-sulfonamide moiety. Docking model of (PDE5: 12d) complex shows that the PDE5-bound conformation of 12d matches completely with that of sildenafil, while 12d is partially overlapped with cGMP with ethyl phosphonate group of 12d superimposed onto the cyclic phosphate group of cGMP.
Abstract: A total of 20 new phenylenedithiourea derivatives was synthesized by reaction of phenylenediisothiocyanates with aromatic amines as aminobenzoic, aminosalicylic acid and their derivatives. Their chemical structures were confirmed by elemental analysis, IR spectrometry and 1H NMR. The compounds were screened for in vitro antifungal, antibacterial activities and some of them have strong antifungal activities comparable to the activity observed for ketoconazole.
