Improving decision making for drug candidates: A computational approach for benzthiazoles as antifungal (original) (raw)
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The three dimensional quantitative structure activity relationships(3D-QSAR) of a series of previously synthesized 1-(1H-1,2,4-triazole-1-yl)-2-(2,4-difluorophenyl)-3-substituted-2-propanol analogs(TDFPP) as antifungal against candida albicans, were studied using kNN(K nearest neighbour) protocol. This was in order to explore the selectivity requirements for fungicidal activity against C. albicans among these congeners. Theoretical active conformers for these TDFPP were generated. The best kNN model(N=44, q2= 0.8650, r2= 0.86504) showed contribution of the steric and electrostatic fields. The models were also external validated using 6 compounds(test set) not included in the model generation process. The statistical parameters from model indicate that the data are well fitted and have predictive ability. Moreover, the resulting contours and isosurface maps provide useful guidance for designing highly active ligands. The model is not only able to predict the activity of new compounds but also explains the important region in the molecules in the quantitative manner.
A series of 56 azole antifungal agents belonging to chemically diverse families related to bifonazole, one of the antimycotic drugs of clinical use, were investigated using the comparative molecular field analysis (CoMFA) paradigm. The studied compounds, which have been already synthesized and reported to be active in vitro against Candida albicans, were divided into a training set and a test set. The training set consisted of 40 molecules from all the different structural classes. Due to the lack of experimental structural data on these derivatives, molecular mechanics techniques were used to obtain putative active conformations for all the compounds. The correctness of this molecular modeling work was confirmed a posteriori by comparison with structural data of the analog 2w obtained by X-ray crystallographic analysis et al. Eur. J. Med. Chem. 1992, 27, 495-502). Two different alignment rules of the training set molecules were used in this study and are based on the assumption that according to published results on azole antifungal agents, all the studied compounds exert their inhibitory activity through the coordination of their azole moiety to the protoporphyrin iron atom of the fungal lanosterol 14R-demethylase enzyme. The predictive ability of each resultant CoMFA model was evaluated using a test set consisting of 16 representative compounds that belong to all the different structural classes. The best 3D-quantitative structure-activity relationship model found yields significant cross-validated, conventional, and predictive r 2 values equal to 0.57, 0.95, and 0.69, respectively. The average absolute error of predictions of this model is 0.30 log units, and the structural moieties of the studied antifungal agents which are thought to contribute to the biological activity were identified. The predictive capability of this model could be exploited in further synthetic studies on antifungal azoles. Furthermore, the results obtained by using two different alignments of the inhibitors suggest that the binding mode of these molecules involves both a coordination to the iron protoporphyrin atom and an additional, likewise relevant, hydrophobic interaction with the active site.
Journal of Chemistry, 2022
Today, fungal infection has become more common disease especially in some cases, such as AIDS, cancer, and organ transplant which the immune system is suppressed. On the other hand, due to the increasing resistance to current antifungal drugs, more and more options for design of novel more efficient compounds with higher resistance are needed. In this study, a series of a fluconazole analogues were subjected to quantitative structure-activity relationship analysis to find the structure requirements for modeling adequate candidate. The best multiple linear regression equation was achieved from GA-PLS and MLR modeling. Subsequently, in silico screening study was applied to found new potent lead compounds based on the resulted model. The ability of the best designed compounds for antifungal activity was investigated by using molecular dynamic (MD) and molecular docking simulation. The results showed that compound F13 can efficiently bind to lanestrol 14-α demethylase target similar to ...
QSAR Studies, Design, Synthesis and Antimicrobial Evaluation of Azole Derivatives
Computational Biology and Bioinformatics, 2014
QSAR analysis of a set of previously synthesized azole derivatives tested for growth inhibitory activity against Candida albicans was performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was used. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q 2 = 0.77 -0.79 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.70 -0.80 for regressions. Biological testing of compounds was performed by disco-diffusion method on solid medium culture versus strain C. albicans ATCC 10231 M885. Most of compounds demonstrated high antifungal activity. Five synthesized compounds also showed activity against clinical isolate strain of C. albicans received from a biological material and resistant to fluconazole.
Results in Chemistry, 2022
The ongoing hunt for novel antimicrobials is necessitated by antibiotic resistance. In the literature, many compounds having a benzothiazole scaffold have been described. They appear effective against Gram(+ve) and Gram( ve) bacteria, also Mycobacterium tuberculosis. The antimicrobial activity of the benzothiazole analogues employed in this investigation was discovered against various bacterial and fungal species. As a result, the current study tried to characterise the essential structural properties of benzothiazole analogues utilizing theoretically based molecular descriptors by QSAR. QSAR model is developed based on a multiple linear regression (MLR) approach using the first 21 analogues out of 40 analogues. Such validated QSAR model with important descriptors is captured by allowable parameters responsible for producing inhibition of bacterial species. This validated QSAR model was used to predict -log(MIC) by using the next 19 benzothiazole analogues out of 40 analogues. After that, these 19 analogues were utilized to explore maximum inhibitory interactions. The docking predicted that compounds 23, 26, 28, 31, 32, 33, 35, 36, and 40 represent interactions with crucial amino acids, like VAL28, VAL613, ARG32, ARG885, PRO43, ASP612 and ARG47 having binding energies -6.77269, -6.99922, -9.72827, -7.00734, -7.54004, -7.38046, -7.0953 and -6.31578 respectively. The compounds 22, 24, 25, 34, 37 and 39 show interactions with less common amino acids, such as ALA25, TYR149, LYS740, HIS617, HIS80, PRO79, and SER3115 with binding energies -7.9874, -6.22435, -6.51044, -5.83977, -8.97108 and -7.38662 respectively towards the target protein. Thus, compounds 23, 26, 28, 31, 32, 33, 35, 36, and 40 show maximum inhibitory interactions. Thus, it is an attempt to Control bacterial infections caused by E. coli. Thus, it is an attempt to Control bacterial infections caused by E. coli. As a consequence, these seven compounds may be used to combat Escherichia coli (Gram(-ve)) Deoxyribonucleic acid gyrase in the future.
Azole Compounds as Inhibitors of Candida albicans: QSAR Modelling
Frontiers in Chemistry, 2021
Candida albicans is a pathogenic opportunistic yeast found in the human gut flora. It may also live outside of the human body, causing diseases ranging from minor to deadly. Candida albicans begins as a budding yeast that can become hyphae in response to a variety of environmental or biological triggers. The hyphae form is responsible for the development of multidrug resistant biofilms, despite the fact that both forms have been associated to virulence Here, we have proposed a linear and SPA-linear quantitative structure activity relationship (QSAR) modeling and prediction of Candida albicans inhibitors. A data set that consisted of 60 derivatives of benzoxazoles, benzimidazoles, oxazolo (4, 5-b) pyridines have been used. In this study, that after applying the leverage analysis method to detect outliers’ molecules, the total number of these compounds reached 55. SPA-MLR model shows superiority over the multiple linear regressions (MLR) by accounting 90% of the Q2 of anti-fungus deri...
Design, synthesis and evaluation of 1, 2, 4-triazole derivatives as antifungal
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A series of 1, 2, 4-triazoles was designed, synthesized and screened for antifungal activity against strain of Aspergillus niger. The design of the triazole compounds was based on docking studies performed on Lanosterol 14α-demethylase an important enzyme required for the synthesis of ergosterol. The three-dimensional QSAR studies of 1, 2, 4-triazole for analysis of the structural requirements for antifungal activity using Vlife MDS 3.5 has been carried out. The negative logarithm of activity (MICs) of the compounds against resistant Aspergillus niger exhibited a strong correlation with the selected 3D molecular descriptors of the triazole analogues. The present findings suggest that the triazole framework is an attractive template for optimization of targeted antifungal activity to achieve better potency and a wider spectrum of activity.
Journal of Chemistry, 2023
Resistance of Candida species is a major problem in the management of Candida infection. Tis study investigated in vivo antifungal activities of several new imidazole and triazole derivatives in a C. albicans systemic infection. Te efcacy of derivatives was determined against systemic infection by C. albicans in mice with cyclophosphamide-induced immunosuppression, and the antifungal activities of the synthesized compounds were evaluated in comparison with fuconazole. Compounds 3 and 8 had the highest efcacy with minimum inhibitory concentration (MIC) values of 0.5-1 μM against the C. albicans pathogen. In vivo activities in immunosuppressed mice were also greater than fuconazole. Furthermore, docking analysis was carried out to know the binding mode of imidazole and triazole derivatives to the CYP51 active site of C. albicans and dihydrofolate reductase as a valid antifungal target. Te docking study found that the antifungal results are well correlated with docking results. ADMET and in silico physicochemical parameters were also performed. Tis study demonstrates that compounds 3 and 8 are potential antifungal candidates against the C. albicans pathogen.
Bioorganic & Medicinal Chemistry Letters, 2010
In an attempt to find novel azole antifungal agents with improved activity and broader spectrum, computer modeling was used to design a series of new azoles with piperidin-4-one O-substituted oxime side chains. Molecular docking studies revealed that they formed hydrophobic and hydrogen-bonding interactions with lanosterol 14a-demethylase of Candida albicans (CACYP51). In vitro antifungal assay indicates that most of the synthesized compounds showed good activity against tested fungal pathogens. In comparison with fluconazole, itraconazole and voriconazole, several compounds (such as 10c, 10e, and 10i) show more potent antifungal activity and broader spectrum, suggesting that they are promising leads for the development of novel antifungal agents.