Your browser doesn't support javascript.
loading
: 20 | 50 | 100
1 - 4 de 4
1.
Int J Mol Sci ; 22(2)2021 Jan 08.
Article En | MEDLINE | ID: mdl-33429999

Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = 0.66 - 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates.


Acinetobacter baumannii/drug effects , Bacterial Infections/drug therapy , Imidazoles/chemistry , Pyridines/chemistry , Acinetobacter baumannii/pathogenicity , Bacterial Infections/microbiology , Drug Resistance, Multiple , Humans , Imidazoles/chemical synthesis , Ionic Liquids/chemical synthesis , Ionic Liquids/chemistry , Pyridines/chemical synthesis , Staphylococcus aureus/drug effects , Staphylococcus aureus/pathogenicity , Structure-Activity Relationship
2.
Comput Biol Chem ; 90: 107407, 2021 Feb.
Article En | MEDLINE | ID: mdl-33191110

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.


Alkaloids/pharmacology , Antifungal Agents/pharmacology , Candida/drug effects , Glutathione Reductase/antagonists & inhibitors , Molecular Docking Simulation , Oxazoles/pharmacology , Alkaloids/chemical synthesis , Alkaloids/chemistry , Antifungal Agents/chemical synthesis , Antifungal Agents/chemistry , Azocines/chemical synthesis , Azocines/chemistry , Azocines/pharmacology , Candida/enzymology , Glutathione Reductase/metabolism , Microbial Sensitivity Tests , Molecular Structure , Oxazoles/chemical synthesis , Oxazoles/chemistry , Quantitative Structure-Activity Relationship , Quinolizines/chemical synthesis , Quinolizines/chemistry , Quinolizines/pharmacology
3.
Chem Biol Drug Des ; 95(6): 624-630, 2020 06.
Article En | MEDLINE | ID: mdl-32168424

QSAR analysis of a set of previously synthesized phosphonium ionic liquids (PILs) tested against Gram-negative multidrug-resistant clinical isolate Acinetobacter baumannii was done using the Online Chemical Modeling Environment (OCHEM). To overcome the problem of overfitting due to descriptor selection, fivefold cross-validation with variable selection in each step of the model development was applied. The predictive ability of the classification models was tested by cross-validation, giving balanced accuracies (BA) of 76%-82%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA = 83%-89%). The models were applied to screen a virtual chemical library with expected activity of compounds against MDR Acinetobacter baumannii. The eighteen most promising compounds were identified, synthesized, and tested. Biological testing of compounds was performed using the disk diffusion method in Mueller-Hinton agar. All tested molecules demonstrated high anti-A. baumannii activity and different toxicity levels. The developed classification SAR models are freely available online at http://ochem.eu/article/113921 and could be used by scientists for design of new more effective antibiotics.


Acinetobacter baumannii/drug effects , Anti-Bacterial Agents/chemistry , Ionic Liquids/chemistry , Organophosphorus Compounds/chemistry , Animals , Anti-Bacterial Agents/pharmacology , Computer Simulation , Crustacea/drug effects , Databases, Chemical , Drug Evaluation, Preclinical , Drug Resistance, Multiple, Bacterial , Humans , Ionic Liquids/pharmacology , Machine Learning , Microbial Sensitivity Tests , Quantitative Structure-Activity Relationship
4.
Curr Drug Discov Technol ; 16(2): 204-209, 2019.
Article En | MEDLINE | ID: mdl-29669499

BACKGROUND: The incidence of invasive fungal infections caused by Candida spp. has increased continuously in recent decades, especially in populations of immunocompromised patients or individuals hospitalized with serious underlying diseases. Therefore, the goal of our study was the search for new potent Candida albicans inhibitors via the development of QSAR models that could speed up this search process. A number of the most promising 1,3-oxazol-4-yltriphenylphosphonium derivatives with predicted activities were synthesized and experimentally tested. Furthermore, the toxicity of the studied compounds was determined in vitro using acetylcholinesterase enzyme as a biological marker. METHODS: The classification QSAR models were created using Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks methods and different combinations of descriptors on the Online Chemical Modeling Environment (OCHEM) platform. Аntifungal properties of the investigated compounds were performed using standard disk diffusion method. The enzyme inhibitory action of the compounds was determined by modified Ellman's method using acetylcholinesterase from the electric organ of Electrophorus electricus. RESULTS: Three classification QSAR models were developed by the WEKA-RF, k-NN and ASNN methods using the ALogPS, E-State indices and Dragon v.7 descriptors. The predictive ability of the models was tested through cross-validation, giving a balanced accuracy BA = 80-91%. All compounds demonstrated good antifungal properties against Candida spp. and slight inhibition of the acetylcholinesterase activity. CONCLUSION: The high percentage of coincidence between the QSAR predictions and the experimental results confirmed the high predictive power of the developed QSAR models that can be applied as tools for finding new potential inhibitors against Candida spp. Furthermore, 1,3-oxazol-4- yl(triphenyl)phosphonium salts could be considered as promising candidates for the treatment of candidiasis and the disinfection of medical equipment.


Antifungal Agents/chemistry , Antifungal Agents/pharmacology , Candida albicans/drug effects , Organophosphorus Compounds/chemistry , Organophosphorus Compounds/pharmacology , Oxazoles/chemistry , Oxazoles/pharmacology , Acetylcholinesterase/metabolism , Antifungal Agents/toxicity , Candida albicans/growth & development , Organophosphorus Compounds/toxicity , Oxazoles/toxicity , Quantitative Structure-Activity Relationship
...