scholarly journals Application of QSAR models in analysis of antibacterial activity of some benzimidazole derivatives against Sarcina lutea

2013 ◽  
Vol 67 (1) ◽  
pp. 27-33
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic ◽  
Lidija Jevric ◽  
Natasa Uzelac
Keyword(s):  
Antibacterial Activity ◽  
Inhibitory Activity ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Log P ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models

In the present paper, a quantitative structure activity relationship (QSAR) has been carried out on a series of 2-methyl and 2-aminobenzimidazole derivatives to identify the lipophilicity requirements for their inhibitory activity against bacteria Sarcina lutea. The tested compounds displayed in vitro antibacterial activity and minimum inhibitory concentration (MIC) was determined for all compounds. The partition coefficients of the studied compounds were measured by the shake flask method (log P) and by theoretical calculation (Clog P). The relationships between lipophilicity descriptors and antibacterial activities were investigated and the mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds. The models were validated by leave-one-out (LOO) technique as well as by the calculation of statistical parameters for the established models. Therefore, QSAR analysis reveals that lipophilicity descriptor govern the inhibitory activity of benzimidazoles studied against Sarcina lutea.

scholarly journals The effect of lipophilicity on the antibacterial activity of some 1-benzylbenzimidazole derivatives

2008 ◽  
Vol 73 (10) ◽  
pp. 967-978 ◽  
Author(s):  
S.O. Podunavac-Kuzmanovic ◽  
D.D. Cvetkovic ◽  
D.J. Barna
Keyword(s):  
Antibacterial Activity ◽  
Quantitative Structure Activity Relationship ◽  
Biologically Active ◽  
Benzimidazole Derivatives ◽  
Log P ◽  
P Values ◽  
High Agreement ◽  
Software Products ◽  
Lipophilicity Parameter ◽  
Qsar Models

In the present paper, the antibacterial activity of some 1-benzylbenzimidazole derivatives were evaluated against the Gram-negative bacteria Escherichia coli. The minimum inhibitory concentration was determined for all the compounds. Quantitative structure-activity relationship (QSAR) was employed to study the effect of the lipophilicity parameters (log P) on the inhibitory activity. Log P values for the target compounds were experimentally determined by the "shake-flask" method and calculated by using eight different software products. Multiple linear regression was used to correlate the log P values and antibacterial activity of the studied benzimidazole derivatives. The results are discussed based on statistical data. The most acceptable QSAR models for the prediction of the antibacterial activity of the investigated series of benzimidazoles were developed. High agreement between the experimental and predicted inhibitory values was obtained. The results of this study indicate that the lipophilicity parameter has a significant effect on the antibacterial activity of this class of compounds, which simplifies the design of new biologically active molecules.

scholarly journals Prediction of the inhibitory activity of benzimidazole derivatives against Bacillus spp.

2011 ◽  
pp. 251-261 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovica ◽  
Dragoljub Cvetkovic ◽  
Slobodan Gadzuric
Keyword(s):  
Antibacterial Activity ◽  
Quantitative Structure Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Calibration Model ◽  
Statistical Parameters ◽  
Gram Positive Bacteria ◽  
Bacillus Spp ◽  
In Vitro Antibacterial Activity ◽  
Leave One Out

In the present paper, the antibacterial activity of some 1-benzylbenzimidazole derivatives was evaluated against Gram-positive bacteria Bacillus spp. by using QSAR (quantitative structure-activity relationship). The tested compounds displayed in vitro antibacterial activity and minimum inhibitory concentration (MIC) was determined for all compounds. The lipophilicity descriptors of the studied compounds were measured by theoretical calculation (ClogP). The correlation between the MIC (log1/cMIC) and lipophilicity descriptors was investigated, and a mathematical model has been developed as a calibration model for predicting the antibacterial activity of this class of compounds. The quality of the model was validated by leave one out (LOO) technique as well as by the calculation of statistical parameters for the established model. The results of the present study may be useful for the designing of new benzimidazole derivatives that would be more potent against Bacillus spp.

RELATIONSHIP BETWEEN THE LIPOPHILICITY AND ANTIFUNGAL ACTIVITY OF SOME BENZIMIDAZOLE DERIVATIVES

2007 ◽  
Vol 06 (04) ◽  
pp. 687-698 ◽  
Author(s):  
SANJA PODUNAVAC-KUZMANOVIĆ ◽  
SINIŠA MARKOV ◽  
DIJANA BARNA
Keyword(s):  
Antifungal Activity ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Log P ◽  
Yeast Saccharomyces Cerevisiae ◽  
Software Products ◽  
Structure Activity ◽  
Lipophilicity Parameters

In the present paper, the antifungal activity of some 1-benzylbenzimidazole derivatives were evaluated against yeast Saccharomyces cerevisiae. The tested compounds displayed in vitro antifungal activity and minimum inhibitory concentration (MIC) was determined for all the compounds. Quantitative structure–activity relationship (QSAR) has been used to study the relationships between inhibitory activity and lipophilicity parameters ( log P). A variety of lipophilicity parameters ( log PHyper, CS log P, mi log P, A log P, IA log P, C log P, log PKow, and X log P) were calculated using different software products, and experimentally determined ("shake-flask" method). On the basis of correlations, the nonlinear structure–activity models were derived between the log 1/cMICand two different lipophilicity parameters. Four high-quality QSAR models were found to have a good predictive ability and a close agreement between the experimental and predicted values was obtained.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4795
Author(s):  
Ajaykumar Gandhi ◽  
Vijay Masand ◽  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Anis Ben Ghorbal ◽  
...  
Keyword(s):  
De Novo ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Structure Activity ◽  
Tnbc Cell ◽  
Qsar Models ◽  
Anti Tumor Activity

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

scholarly journals Correlations between the lipophilicity and the inhibitory activity of different substituted benzimidazoles

2009 ◽  
Vol 15 (3) ◽  
pp. 125-130 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic ◽  
Dijana Barna
Keyword(s):  
Antibacterial Activity ◽  
Inhibitory Activity ◽  
Theoretical Models ◽  
Qsar Model ◽  
Calibration Model ◽  
Complete Regression ◽  
Statistical Parameters ◽  
High Agreement ◽  
Office Software

2-Amino and 2-methylbenzimidazole derivatives were tested in vitro for their inhibitory activity against the bacteria Bacillus cereus. The minimum inhibitory concentration (MIC) was determined for all compounds. The lipophilicity descriptors were calculated by using CS Chem-Office Software, version 7.0. The stepwise regression method was used to derive the most significant model as a calibration model for predicting the antibacterial activity of this class of compounds. A complete regression analysis resorting to linear and quadratic relationships was made. Theoretical models were validated by leaving one out (LOO) technique, as well as by the calculation of statistical parameters for the established models. The best QSAR model for the prediction of an inhibitory activity of the investigated series of benzimidazoles was developed. A high agreement between the experimental and predicted inhibitory values was obtained. The results indicated that the antibacterial activity could be modeled using the lipophilicity descriptor.

scholarly journals In vitro Antibacterial Activities of Honey Bee Extracts against Bacterial Isolates of Wound Infections

2021 ◽  
pp. 20-25
Author(s):  
O. A. Ayodele ◽  
J. O. Aribisala ◽  
A. T. Oseni ◽  
M. K. Oladunmoye
Keyword(s):  
Antibacterial Activity ◽  
Honey Bee ◽  
Honey Bees ◽  
Inhibitory Activity ◽  
Ethanolic Extract ◽  
Antibacterial Activities ◽  
Diffusion Method ◽  
Wound Infections ◽  
Zones Of Inhibition

Microorganisms most especially bacteria, continue to develop resistance against antimicrobial agents; hence novel sources of antibiotics are urgently needed to reduce this problem. This study was carried out to investigate the antibacterial activities of ethanolic, chloroform and aqueous extracts of Apis mellifera (honey bee) on isolates of wound infections. The isolates used in this study were procured from University of Ilorin Teaching Hospital (UITH) and confirmed using morphological and biochemical tests. The isolates used include; Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pnuemoniae, Proteus mirabilis and Proteus vulgaris. Honey bees were collected from an apitherapist at Sunshine honey and agro foods, Akure, Ondo State Nigeria. The whole insect was used for in vitro antibacterial evaluation of the isolates using agar well diffusion method. Ethanolic extract of A. mellifera had the highest inhibitory activity with mean zones of inhibition ranging from 7.40 mm to 21.67 mm, chloroform extracts had moderate inhibitory activity ranging from 4.63 mm to 10.03 mm while the aqueous extract had the least activity with zones of inhibition ranging from 3.00 mm to 6.30 mm. However, no antibacterial activity was observed against P. aeruginosa for all the extracts. It is concluded that extracts of honey bees most especially the ethanolic extract have antibacterial activity and thus could be a potential antibacterial agent against isolates of wound infections.

scholarly journals Antibacterial activity of cobalt(II) complexes with some benzimidazole derivatives

2008 ◽  
Vol 73 (12) ◽  
pp. 1153-1160 ◽  
Author(s):  
S.O. Podunavac-Kuzmanovic ◽  
V.M. Leovac ◽  
D.D. Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Chemical Structure ◽  
Antibacterial Activities ◽  
Benzimidazole Derivatives ◽  
Wall Structure ◽  
Gram Negative Bacteria ◽  
Bacterial Strains ◽  
Gram Positive ◽  
Gram Negative

The antibacterial activities of cobalt(II) complexes with two series of benzimidazoles were evaluated in vitro against three Gram-positive bacterial strains (Bacillus cereus, Staphylococcus aureus, and Sarcina lutea) and one Gram-negative isolate (Pseudomonas aeruginosa). The minimum inhibitory concentration was determined for all the complexes. The majority of the investtigated complexes displayed in vitro inhibitory activity against very persistent bacteria. They were found to be more active against Gram-positive than Gram-negative bacteria. It may be concluded that the antibacterial activity of the compounds is related to the cell wall structure of the tested bacteria. Comparing the inhibitory activities of the tested complexes, it was found that the 1-substituted- -2-aminobenzimidazole derivatives were more active than complexes of 1-substituted- 2-amino-5,6-dimethylbenzimidazoles. The effect of chemical structure on the antibacterial activity is discussed.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Quantitative structure-activity relationships to predict antibacterial effect of some benzimidazole derivatives

2008 ◽  
pp. 181-191 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dijana Barna ◽  
Dragoljub Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Biologically Active ◽  
Benzimidazole Derivatives ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Lipophilicity Parameter ◽  
Office Software

The antibacterial activity of some substituted benzimidazole derivatives against Gram negative bacteria Escherichia coli was investigated. The tested compounds displayed in vitro inhibitory activity and their minimum inhibitory concentrations were determined. Quantitative structure-activity relationship has been used to study the relationships between the antibacterial activity and lipophilicity parameter, logP. Lipophilicity parameters were calculated for each molecule by using CS Chem-Office Software version 7.0. Multiple linear regression was used to correlate the logP values and antibacterial activity of benzimidazole derivatives. The results are discussed on the basis of statistical data. The most acceptable QSAR model for prediction of antibacterial activity of the investigated series of benzimidazoles was developed. High agreement between experimental and predicted inhibitory values was obtained. The results of this study indicate that the lipophilicity parameter has a significant effect on antibacterial activity of this class of compounds, thus simplifying design of new biologically active molecules.

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