1,3-Oxazole derivatives of cytisine as potential inhibitors of glutathione reductase of Candida spp.: QSAR modeling, docking analysis and experimental study of new anti-Candida agents.

Natural products as well as their derivatives play a significant role in the discovery of new biologically active compounds in the different areas of our life especially in the field of medicine. The synthesis of compounds produced from natural products including cytisine is one approach for the wider use of natural substances in the development of new drugs. QSAR modeling was used to predict and select of biologically active cytisine-containing 1,3-oxazoles. The eleven most promising compounds were identified, synthesized and tested. The activity of the synthesized compounds was evaluated using the disc diffusion method against C. albicans M 885 (ATCC 10,231) strain and clinical fluconazole-resistant Candida krusei strain. Molecular docking of the most active compounds as potential inhibitors of the Candida spp. glutathione reductase was performed using the AutoDock Vina. The built classification models demonstrated good stability, robustness and predictive power. The eleven cytisine-containing 1,3-oxazoles were synthesized and their activity against Candida spp. was evaluated. Compounds 10, 11 as potential inhibitors of the Candida spp. glutathione reductase demonstrated the high activity against C. albicans M 885 (ATCC 10,231) strain and clinical fluconazole-resistant Candida krusei strain. The studied compounds 10, 11 present the interesting scaffold for further investigation as potential inhibitors of the Candida spp. glutathione reductase with the promising antifungal properties. The developed models are publicly available online at http://ochem.eu/article/120720 and could be used by scientists for design of new more effective drugs.

Reduced ecotoxicity and improved biodegradability of cationic biocides based on ester-functionalized pyridinium ionic liquids.

Ester-functionalized pyridinium ionic liquids (ILs), 1-decyloxycarbonylmethylpyridinium chloride (PyrСOOC10-Cl), and 1-dodecyloxycarbonylmethylpyridinium chloride (PyrСOOC12-Cl) have been synthesized and studied for their environmental toxicity. Simple long-chain pyridinium ILs, 1-dodecylpyridinium chloride (PyrC12-Cl), and commercial disinfectant cetylpyridinium chloride (CPC) were used as reference compounds. Both ester-functionalized ILs and CPC showed significantly reduced antibacterial activity compared to PyrC12-Cl. However, ester-functionalized ILs were found to have excellent antifungal activity towards Candida albicans fungus strains, similar to PyrC12-Cl and much higher than for CPC. The molecular docking of ILs in the active site of the known antifungal target N-myristoyltransferase (Nmt) C. albicans has been conducted. The obtained results indicate the possibility of ILs binding into the Nmt pocket. The high stability of the complexes, especially for PyrCOOC10-Cl, is ensured by hydrogen bonding, electrostatic anion-pi interactions, as well as hydrophobic pi-alkyl and alkyl interactions that was confirmed by calculated binding energy values. The acute toxicity studies of ester-functionalized ILs on D. rerio (zebrafish) hydrobiont have shown their dramatically reduced ecotoxicity compared to PyrC12-Cl and CPC. Thus, LD50 values of 15.2 mg/L and 16.8 mg/L were obtained for PyrCOOC10-Cl and PyrCOOC12-Cl, respectively, whereas CPC had LD50 value of 0.018 mg/L. The primary biodegradation test CEC L-33-A93 of ILs indicated an improved biodegradability of ester-functionalized compounds compared to simple long-chain ILs. Based on the obtained results, PyrCOOC10-Cl may be considered as very promising cationic biocide due to the combination of soft antimicrobial activity and reduced ecotoxicity, as well as improved biodegradability.

Imidazolium ionic liquids as effective antiseptics and disinfectants against drug resistant S. aureus: In silico and in vitro studies.

This paper describes Quantitative Structure-Activity Relationships (QSAR) studies, molecular docking and in vitro antibacterial activity of several potent imidazolium-based ionic liquids (ILs) against S. aureus ATCC 25923 and its clinical isolate. Small set of 131 ILs was collected from the literature and uploaded in the OCHEM database. QSAR methodologies used Associative Neural Networks and Random Forests (WEKA-RF) methods. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.82-0.87 for regression models and overall prediction accuracies of 80-82.1% for classification models. The proposed QSAR models are freely available online on OCHEM server at https://ochem.eu/article/107364 and can be used for estimation of antibacterial activity of new imidazolium-based ILs. A series of synthesized 1,3-dialkylimidazolium ILs with predicted activity were evaluated in vitro. The high activity of 7 ILs against S. aureus strain and its clinical isolate was measured and thereafter analyzed by the molecular docking to prokaryotic homologue of a eukaryotic tubulin FtsZ.

QSAR Studies, Synthesis and Antibacterial Assessment of New Inhibitors Against Multidrug-Resistant Mycobacterium tuberculosis.

BACKGROUND: The increasing rate of appearance of multidrug-resistant strains of Mycobacterium tuberculosis (MDR-TB) is a serious problem at the present time. MDR-TB forms do not respond to the standard treatment with the commonly used drugs and can take some years or more to treat with drugs that are less potent, more toxic and much more expensive. OBJECTIVE: The goal of this work is to identify the novel effective drug candidates active against MDR-TB strains through the use of methods of cheminformatics and computeraided drug design. METHODS: This paper describes Quantitative Structure-Activity Relationships (QSAR) studies using Artificial Neural Networks, synthesis and in vitro antitubercular activity of several potent compounds against H37Rv and resistant Mycobacterium tuberculosis (Mtb) strains. RESULTS: Eight QSAR models were built using various types of descriptors with four publicly available structurally diverse datasets, including recent data from PubChem and ChEMBL. The predictive power of the obtained QSAR models was evaluated with a cross-validation procedure, giving a q2=0.74-0.78 for regression models and overall accuracy 78.9-94.4% for classification models. The external test sets were predicted with accuracies in the range of 84.1-95.0% (for the active/inactive classifications) and q2=0.80- 0.83 for regressions. The 15 synthesized compounds showed inhibitory activity against H37Rv strain whereas the compounds 1-7 were also active against resistant Mtb strain (resistant to isoniazid and rifampicin). CONCLUSION: The results indicated that compounds 1-7 could serve as promising leads for further optimization as novel antibacterial inhibitors, in particular, for the treatment of drug resistance of Mtb forms.

Imidazolium Ionic Liquids as Potential Anti-Candida Inhibitors: QSAR Modeling and Experimental Studies.

Quantitative structure-activity relationships (QSAR) of imidazolium ionic liquids (ILs) as inhibitors of C. albicans collection strains (IOA-109, KCTC 1940, ATCC 10231) have been studied. Predictive QSAR models were built using different descriptor sets for a set of 88 ionic liquids with known minimum inhibitory concentrations (MIC) against C. albicans. We applied the state-of-the-art QSAR methodologies such as WEKA Random Forest (RF) as a binary classifier, Associative Neural Networks (ASNN) and k-Nearest Neighbors (k-NN) to build continuum non-linear regression models. The obtained models were validated using a 5-fold cross-validation approach and resulted in the prediction accuracies of 80% ± 5.0 for the classification models and q2 = 0.73-0.87 for the non-linear regression models. Biological testing of newly synthesized 1,3-dialkylimidazolium ionic liquids with predicted activity was performed by disco-diffusion method against C. albicans ATCC 10231 M885 strain and clinical isolates C. albicans, C. krusei and C. glabrata strains. The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models within the applicability domain of new imidazolium ionic liquids.

Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials.