ABSTRACT
QSAR studies on the number of compounds tested as S. aureus inhibitors were performed using an interactive Online Chemical Database and Modeling Environment (OCHEM) web platform. The predictive ability of the developed consensus QSAR model was q2=0.79±0.02. The consensus prediction for the external evaluation set afforded high predictive power (q2=0.82±0.03). The models were applied to screen a virtual chemical library with anti-S. aureus activity. Six promising new bicyclic trifluoromethylated pyrroles were identified, synthesized and evaluated inâ vitro against S. aureus, E. coli, and A. baumannii for their antibacterial activity and against C. albicans, C. krusei and C. glabrata for their antifungal activity. The synthesized compounds were characterized by 1H, 19F, and 13Câ NMR and elemental analysis. The antimicrobial activity assessment indicated that trifluoromethylated pyrroles 9 and 11 demonstrated the greatest antibacterial and antifungal effects against all the tested pathogens, especially against multidrug-resistant strains. The acute toxicity of the compounds to Daphnia magna ranged from 1.21 to 33.39â mg/L (moderately and slightly toxic). Based on the docking results, it can be suggested that the antibacterial and antifungal effects of the compounds can be explained by the inhibition of bacterial wall component synthesis.
Subject(s)
Anti-Bacterial Agents , Antifungal Agents , Microbial Sensitivity Tests , Pyrroles , Quantitative Structure-Activity Relationship , Antifungal Agents/pharmacology , Antifungal Agents/chemistry , Antifungal Agents/chemical synthesis , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/chemical synthesis , Pyrroles/chemistry , Pyrroles/pharmacology , Pyrroles/chemical synthesis , Staphylococcus aureus/drug effects , Drug Design , Escherichia coli/drug effects , Animals , Molecular Structure , Daphnia/drug effects , Molecular Docking Simulation , Acinetobacter baumannii/drug effects , Computer Simulation , Candida albicans/drug effectsABSTRACT
Long-chain imidazole-based ionic liquids (compounds 2, 4, 9) and lysosomotropic detergents (compounds 7, 3, 8) with potent anticancer activity were synthesized. Their inhibitory activities against neuroblastoma and leukaemia cell lines were predicted by the new in silico QSAR models. The cytotoxic activities of the synthesized imidazole derivatives were investigated on the SK-N-DZ (human neuroblastoma) and K-562 (human chronic myeloid leukaemia) cell lines. Compounds 2 and 7 showed the highest in vitro cytotoxic effect on both cancer cell lines. The docking procedure of compounds 2 and 7 into the NAD+ coenzyme binding site of deacetylase Sirtuin-1 (SIRT-1) showed the formation of protein-ligand complexes with calculated binding energies of - 8.0 and - 8.1 kcal/mol, respectively. The interaction of SIRT1 with compounds 2, 7 and 9 and the interaction of Bromodomain-containing protein 4 (BRD4) with compounds 7 and 9 were also demonstrated by thermal shift assay. Compounds 2, 4, 7 and 9 inhibited SIRT1 deacetylase activity in the SIRT-Glo assay. Compounds 7 and 9 showed a moderate inhibitory activity against Aurora kinase A. In addition, compounds 3, 4, 8 and 9 inhibited the Janus kinase 2 activity. The results obtained showed that long-chain imidazole derivatives exhibited cytotoxic activities on K562 leukaemia and SK-N-DZ neuroblastoma cell lines. Furthermore, these compounds inhibited a panel of molecular targets involved in leukaemia and neuroblastoma tumorigenesis. All these results suggest that both long-chain imidazole-based ionic liquids and lysosomotropic detergents may be an effective alternative for the treatment of neuroblastoma and chronic myeloid leukemia and merit further investigation.
ABSTRACT
A novel series of N-(4-cyano-1,3-oxazol-5-yl)sulfonamides have been synthesized and characterized by IR, 1 H NMR, 13 C NMR spectroscopy, elemental analysis and chromato-mass-spectrometry. The anticancer activities of all newly synthesized compounds were evaluated via a single high-dose assay (10â µM) against 60 cancer cell lines by the National Cancer Institute (USA) according to its screening protocol. Among them, compounds 2 and 10 exhibited the highest activity against the 60 cancer cell lines panel in the one-dose assay. Compounds 2 and 10 showed inhibitory activity within the GI50 parameter and in five dose analyses. However, their cytostatic activity was only observed against some cancer cell lines, and cytotoxic concentration was outside the maximum used, i. e., >100â µM. The COMPARE analysis showed that the average graphs of the tested compounds have a moderate positive correlation with compounds with the L-cysteine analog and vinblastine (GI50 ) as well as paclitaxel (TGI), which target microtubules. Therefore, disruption of microtubule formation may be one of the mechanisms of the anticancer activity of the tested compounds, especially since among tubulin inhibitors with antitumor activity, compounds with an oxazole motif are widely represented. Therefore, N-(4-cyano-1,3-oxazol-5-yl)sulfonamides may be promising for further functionalization to obtain more active compounds.
Subject(s)
Antineoplastic Agents , Neoplasms , Antineoplastic Agents/chemistry , Cell Line, Tumor , Cell Proliferation , Drug Screening Assays, Antitumor , Early Detection of Cancer , Molecular Structure , Structure-Activity Relationship , Sulfanilamide/pharmacology , Sulfonamides/chemistry , HumansABSTRACT
New substituted imidazolidinone sulfonamides have been developed using a rational drug design strategy. Predictive QSAR models for the search of new antibacterials were created using the OCHEM platform. Regression models were applied to verify a virtual chemical library of new imidazolidinone derivatives designed to have antibacterial activity. A number of substituted imidazolidinone sulfonamides as effective antibacterial agents were identified by QSAR prediction, synthesized and characterized by spectral and elemental, and tested inâ vitro. Six studied compounds have shown the highest inâ vitro antibacterial activity against Gram-negative E. coli and Gram-positive S. aureus multidrug-resistant strains. The inâ vivo acute toxicity of these imidazolidinone sulfonamides based on the LC50 value ranged from 16.01 to 44.35â mg/L (slightly toxic compounds class). The results of molecular docking suggest that the antibacterial mechanism of the compounds can be associated with the inhibition of post-translational modification processes of bacterial peptides and proteins.
Subject(s)
Anti-Bacterial Agents , Staphylococcus aureus , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/chemistry , Molecular Docking Simulation , Sulfonamides/pharmacology , Sulfonamides/chemistry , Escherichia coli , Sulfanilamide , Microbial Sensitivity TestsABSTRACT
To develop novel antimicrobial agents a series of 2(4)-hydrazone derivatives of quinoline were designed, synthesized and tested. QSAR models of the antibacterial activity of quinoline derivatives were developed by the OCHEM web platform using different machine learning methods. A virtual set of quinoline derivatives was verified with a previously published classification model of anti-E. coli activity and screened using the regression model of anti-S. aureus activity. Selected and synthesized 2(4)-hydrazone derivatives of quinoline exhibited antibacterial activity against the standard and antibiotic-resistant S. aureus and E. coli strains in the range from 15 to 30â mm by the diameter of growth inhibition zones. Molecular docking showed the complex formation of the studied compounds into the catalytic domain of dihydrofolate reductase with an estimated binding affinity from -8.4 to -9.4â kcal/mol.
Subject(s)
Methicillin-Resistant Staphylococcus aureus , Quinolines , Hydrazones/pharmacology , Molecular Docking Simulation , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/chemistry , Quinolines/pharmacology , Quinolines/chemistry , Microbial Sensitivity Tests , Structure-Activity RelationshipABSTRACT
QSAR analysis of previously synthesized and nature-inspired virtual isoflavone-cytisine hybrids against the HEp-2 laryngeal carcinoma cell lines was performed using the OCHEM web platform. 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 such as 8-cytisinylmethyl derivatives of 5,7- and 6,7-dihydroxyisoflavones. The synthetic procedure for selective aminomethylation of 5,7-dihydroxyisoflavones with cytisine was developed. In vitro testing identified compound 7 f with cisplatin-level cytotoxicity against HEp-2â cell lines and compound 10 which was twice active than cisplatin after 72â h of incubation.
Subject(s)
Alkaloids , Antineoplastic Agents , Carcinoma , Isoflavones , Humans , Cisplatin , Antineoplastic Agents/pharmacology , Isoflavones/pharmacology , Mannich Bases , Structure-Activity Relationship , Alkaloids/pharmacology , Cell Line, TumorABSTRACT
A previously developed model to predict antibacterial activity of ionic liquids against a resistant A. baumannii strain was used to assess activity of phosphonium ionic liquids. Their antioxidant potential was additionally evaluated with newly developed models, which were based on public data. The accuracy of the models was rigorously evaluated using cross-validation as well as test set prediction. Six alkyl triphenylphosphonium and alkyl tributylphosphonium bromides with the C8, C10, and C12 alkyl chain length were synthesized and tested in vitro. Experimental studies confirmed their activity against A. baumannii as well as showed pronounced antioxidant properties. These results suggest that phosphonium ionic liquids could be promising lead structures against A. baumannii.
ABSTRACT
Predictive QSAR models for the search of new adenosine A2A receptor antagonists were developed by using OCHEM platform. The predictive ability of the regression models has coefficient of determination q2 = 0.65-0.71 with cross-validation and independent test set. The inhibition activities of novel fused 7-deazaxanthine compounds were predicted by the developed QSAR models. A preparative method for the synthesis of pyrimido[5',4':4,5]pyrrolo[1,2-a][1,4]diazepine derivatives was developed, and 11 new adenosine A2A receptor antagonists were obtained. Preliminary investigations into the toxicology of fused 7-deazaxanthine compounds toward commonly used model organism to assess toxicity invertebrate cladoceran D. magna were also described.
Subject(s)
Quantitative Structure-Activity Relationship , Receptor, Adenosine A2A , Molecular Docking Simulation , Adenosine , Adenosine A2 Receptor Antagonists/pharmacologyABSTRACT
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.
Subject(s)
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 RelationshipABSTRACT
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.
Subject(s)
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/pharmacologyABSTRACT
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.
Subject(s)
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 RelationshipABSTRACT
Spread of multidrug-resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the development of new effective inhibitory compounds with selective molecular mechanism of action and low toxicity. The goal of this work is to identify more potent molecules active against E. coli strains by using machine learning, docking studies, synthesis and biological evaluation. A set of predictive QSAR models was built with two publicly available structurally diverse data sets, including recent data deposited in PubChem. The predictive ability of these models tested by a 5-fold cross-validation, resulted in balanced accuracies (BA) of 59-98% for the binary classifiers. Test sets validation showed that the models could be instrumental in predicting the antimicrobial activity with an accuracy (with BA = 60-99 %) within the applicability domain. The models were applied to screen a virtual chemical library, which was designed to have activity against resistant E. coli strains. The eight most promising compounds were identified, synthesized and tested. All of them showed the different levels of anti-E. coli activity and acute toxicity. The docking results have shown that all studied compounds are potential DNA gyrase inhibitors through the estimated interactions with amino acid residues and magnesium ion in the enzyme active center The synthesized compounds could be used as an interesting starting point for further development of drugs with low toxicity and selective molecular action mechanism against resistant E. coli strains. The developed QSAR models are freely available online at OCHEM http://ochem.eu/article/112525 and can be used to virtual screening of potential compounds with anti-E. coli activity.
Subject(s)
Anti-Bacterial Agents/pharmacology , DNA Gyrase/metabolism , Drug Design , Escherichia coli/drug effects , Machine Learning , Molecular Docking Simulation , Quinolines/pharmacology , Topoisomerase II Inhibitors/pharmacology , Anti-Bacterial Agents/chemical synthesis , Anti-Bacterial Agents/chemistry , Computational Biology , Escherichia coli/enzymology , Microbial Sensitivity Tests , Molecular Structure , Quantitative Structure-Activity Relationship , Quinolines/chemical synthesis , Quinolines/chemistry , Topoisomerase II Inhibitors/chemical synthesis , Topoisomerase II Inhibitors/chemistryABSTRACT
BACKGROUND: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. OBJECTIVE: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. METHODS: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. RESULTS: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61-78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67-79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10-100 mg/L) and one as practically harmless (LD50 in the range of 100-1000 mg/L). CONCLUSION: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.
Subject(s)
Antitubercular Agents/pharmacology , Drug Design , Machine Learning , Mycobacterium tuberculosis/drug effects , Tuberculosis, Multidrug-Resistant/drug therapy , Animals , Antitubercular Agents/chemistry , Antitubercular Agents/therapeutic use , Daphnia , Datasets as Topic , Humans , Isoniazid/pharmacology , Isoniazid/therapeutic use , Microbial Sensitivity Tests , Models, Chemical , Rifampin/pharmacology , Rifampin/therapeutic use , Toxicity Tests, Acute , Tuberculosis, Multidrug-Resistant/microbiologyABSTRACT
In this study, the synthesis, in vitro anti-Candida activity and molecular modeling of 4-phosphorylated derivatives of 1,3-oxazole as inhibitors of Candida albicans fructose-1,6-bisphosphate aldolase (FBA-II) are demonstrated and discussed. Significant similarity of the primary and secondary structure, binding sites and active sites of FBA-II C. albicans and Mycobacterium tuberculosis are established. FBA-II C. albicans inhibitors contained 1,3-oxazole-4-phosphonates moiety are created by analogy to inhibitors FBA-II M. tuberculosis. The experimental studies of the anti-Candida activity of the designed and synthesized compounds have shown their high activity against standard strain and its C. albicans fluconazole resistant clinical isolate. It was hypothesized that the growth suppression of fluconazole-resistant С. albicans strain may be due to the inhibition of aldolase fructose-1,6-bisphosphate. A qualitative homology 3D model of the C. albicans FBA-II was created using SWISS-MODEL server. The probable mechanism of FBA-II inhibition by studied 4-phosphorylated derivatives was shown using molecular docking. The main role of amino acid residues His110, His226, Gly227, Leu248, Val238, Asp144, Lys230, Glu147, Gly227, Ala112, Leu145 and catalytic zinc atom in the formation of stable ligand-protein complexes with ΔG = -6.89, -7.2, -7.16, -7.5, -8.0, -7.9 kcal/mol was shown. Thus, the positive results obtained in the work were demonstrated the promise of using the proposed homology 3D model of the C. albicans FBA-II as the target for the search and development of new anti-Candida agents against azole-resistant fungal pathogens. Designed and studied 4-phosphorylated derivatives of 1,3-oxazole having a direct inhibiting FBA-II molecular mechanism of action can be used as perspective drug-candidates against resistant C. albicans strains.
ABSTRACT
Based on modern literature data about biological activity of E7010 derivatives, a series of new sulfonamides as potential anticancer drugs were rationally designed by QSAR modeling methods Сlassification learning QSAR models to predict the tubulin polymerization inhibition activity of novel sulfonamides as potential anticancer agents were created using the Online Chemical Modeling Environment (OCHEM) and are freely available online on OCHEM server at https://ochem.eu/article/107790. A series of sulfonamides with predicted activity were synthesized and tested against 60 human cancer cell lines with growth inhibition percent values. The highest antiproliferative activity against leukemia (cell lines K-562 and MOLT-4), non-small cell lung cancer (cell line NCI-H522), colon cancer (cell lines NT29 and SW-620), melanoma (cell lines MALME-3M and UACC-257), ovarian cancer (cell lines IGROV1 and OVCAR-3), renal cancer (cell lines ACHN and UO-31), breast cancer (cell line T-47D) was found for compounds 4-9. According to the docking results the compounds 4-9 induce cytotoxicity by the disruption of the microtubule dynamics by inhibiting tubulin polymerization via effective binding into colchicine domain, similar the E7010.
Subject(s)
Antineoplastic Agents/pharmacology , Drug Design , Sulfonamides/pharmacology , Antineoplastic Agents/chemical synthesis , Antineoplastic Agents/chemistry , Cell Line, Tumor , Cell Proliferation/drug effects , Drug Screening Assays, Antitumor , Humans , Machine Learning , Models, Molecular , Molecular Structure , Quantitative Structure-Activity Relationship , Sulfonamides/chemical synthesis , Sulfonamides/chemistryABSTRACT
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 q2â¯=â¯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.
Subject(s)
Anti-Infective Agents, Local/pharmacology , Disinfectants/pharmacology , Imidazoles/pharmacology , Ionic Liquids/pharmacology , Machine Learning , Methicillin-Resistant Staphylococcus aureus/drug effects , Anti-Infective Agents, Local/chemistry , Disinfectants/chemistry , Imidazoles/chemistry , Ionic Liquids/chemistry , Neural Networks, Computer , Quantitative Structure-Activity RelationshipABSTRACT
The problem of designing new antitubercular drugs against multiple drug-resistant tuberculosis (MDR-TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR-TB, we collected a large literature data set and developed models against the non-resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2 = .7-.8 (regression models) and balanced accuracies of about 80% (classification models) with cross-validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR-TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR-TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti-TB activity of new chemicals.
Subject(s)
Antitubercular Agents/chemical synthesis , Drug Design , Isoniazid/chemistry , Antitubercular Agents/pharmacology , Antitubercular Agents/therapeutic use , Bacterial Proteins/chemistry , Bacterial Proteins/metabolism , Binding Sites , Catalytic Domain , Humans , Isoniazid/pharmacology , Isoniazid/therapeutic use , Machine Learning , Microbial Sensitivity Tests , Molecular Docking Simulation , Mycobacterium tuberculosis/drug effects , Mycobacterium tuberculosis/metabolism , Oxidoreductases/chemistry , Oxidoreductases/metabolism , Tuberculosis, Multidrug-Resistant/drug therapy , Tuberculosis, Multidrug-Resistant/pathologyABSTRACT
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.
Subject(s)
Antitubercular Agents/chemistry , Antitubercular Agents/pharmacology , Mycobacterium tuberculosis/drug effects , Quantitative Structure-Activity Relationship , Models, Molecular , Mycobacterium tuberculosis/growth & development , Neural Networks, Computer , Tuberculosis, Multidrug-Resistant/drug therapyABSTRACT
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.
Subject(s)
Antifungal Agents/pharmacology , Candida albicans/drug effects , Imidazoles/pharmacology , Ionic Liquids/pharmacology , Models, Molecular , Antifungal Agents/chemistry , Candida albicans/growth & development , Imidazoles/chemistry , Ionic Liquids/chemistry , Machine Learning , Neural Networks, Computer , Quantitative Structure-Activity Relationship , Regression Analysis , Reproducibility of ResultsABSTRACT
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.