RESUMEN
A series of novel 2-oxoimidazolidine derivatives were synthesized and their antiviral activities against BK human polyomavirus type 1 (BKPyV) were evaluated inâ vitro. Bioassays showed that the synthesized compounds 1-{[(4E)-5-(dichloromethylidene)-2-oxoimidazolidin-4-ylidene]sulfamoyl}piperidine-4-carboxylic acid (5) and N-Cyclobutyl-N'-[(4E)-5-(dichloromethylidene)-2-oxoimidazolidin-4-ylidene]sulfuric diamide (4) exhibited moderate activities against BKPyV (EC50 =5.4 and 5.5â µm, respectively) that are comparable to the standard drug Cidofovir. Compound 5 exhibited the same cytotoxicity in HFF cells and selectivity index (SI50 ) as Cidofovir. The selectivity index of compound 4 is three times less than that of Cidofovir due to the higher toxicity of this compound. Hence, these compounds may be taken as lead compound for further development of novel ant-BKPyV agents.
Asunto(s)
Antivirales/farmacología , Virus BK/efectos de los fármacos , Cidofovir/farmacología , Diseño de Fármacos , Imidazolidinas/farmacología , Antivirales/síntesis química , Antivirales/química , Supervivencia Celular/efectos de los fármacos , Células Cultivadas , Cidofovir/química , Relación Dosis-Respuesta a Droga , Humanos , Imidazolidinas/síntesis química , Imidazolidinas/química , Pruebas de Sensibilidad Microbiana , Relación Estructura-Actividad , Replicación Viral/efectos de los fármacosRESUMEN
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.
Asunto(s)
Acinetobacter baumannii/efectos de los fármacos , Antibacterianos/química , Líquidos Iónicos/química , Compuestos Organofosforados/química , Animales , Antibacterianos/farmacología , Simulación por Computador , Crustáceos/efectos de los fármacos , Bases de Datos de Compuestos Químicos , Evaluación Preclínica de Medicamentos , Farmacorresistencia Bacteriana Múltiple , Humanos , Líquidos Iónicos/farmacología , Aprendizaje Automático , Pruebas de Sensibilidad Microbiana , Relación Estructura-Actividad CuantitativaRESUMEN
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.