scholarly journals Quantitative Structure–Activity Relationships for the Flavonoid-Mediated Inhibition of P-Glycoprotein in KB/MDR1 Cells

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1661 ◽  
Author(s):  
Mengmeng Xia ◽  
Yajing Fang ◽  
Weiwei Cao ◽  
Fuqiang Liang ◽  
Siyi Pan ◽  
...  
Keyword(s):  
Inhibitory Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Glycoprotein P ◽  
Chemotherapy Drugs ◽  
Structure Activity ◽  
P Glycoprotein ◽  
Daily Diet ◽  
Low Toxicity

P-glycoprotein (P-gp) serves as a therapeutic target for the development of inhibitors to overcome multidrug resistance (MDR) in cancer cells. In order to enhance the uptake of chemotherapy drugs, larger amounts of P-gp inhibitors are required. Besides several chemically synthesized P-gp inhibitors, flavonoids as P-gp inhibitors are being investigated, with their advantages including abundance in our daily diet and a low toxicity. The cytotoxicity of daunorubicin (as a substrate of P-gp) to KB/MDR1 cells and the parental KB cells was measured in the presence or absence of flavonoids. A two-dimensional quantitative structure–activity relationship (2D-QSAR) model was built with a high cross-validation coefficient (Q2) value of 0.829. Descriptors including vsurf_DW23, E_sol, Dipole and vsurf_G were determined to be related to the inhibitory activity of flavonoids. The lack of 2,3-double bond, 3′-OH, 4′-OH and the increased number of methoxylated substitutions were shown to be beneficial for the inhibition of P-gp. These results are important for the screening of flavonoids for inhibitory activity on P-gp.

scholarly journals Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Toshio Kasamatsu ◽  
Airi Kitazawa ◽  
Sumie Tajima ◽  
Masahiro Kaneko ◽  
Kei-ichi Sugiyama ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Ames Test ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Low Molecular Weight ◽  
Quantitative Structure ◽  
Industrial Chemicals ◽  
Structure Activity

Abstract Background Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure–activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. Results In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals’ Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model “StarDrop NIHS 834_67” showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. Conclusions A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

2021 ◽  
Vol 16 (10) ◽  
pp. 50-58
Author(s):  
Ali Qusay Khalid ◽  
Vasudeva Rao Avupati ◽  
Husniza Hussain ◽  
Tabarek Najeeb Zaidan
Keyword(s):  
Dengue Fever ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Contour Maps ◽  
Structure Activity ◽  
Bioactive Ligands

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.

Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis

RSC Advances ◽  
2015 ◽  
Vol 5 (70) ◽  
pp. 57030-57037 ◽  
Author(s):  
Arafeh Bigdeli ◽  
Mohammad Reza Hormozi-Nezhad ◽  
Hadi Parastar
Keyword(s):  
Gold Nanoparticles ◽  
Gold Nanoparticle ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Determining Factors

A nano-quantitative structure-activity relationship (nano-QSAR) model is proposed to indicate the determining factors responsible in the exocytosis of gold nanoparticles in macrophages.

scholarly journals Conceptual Framework for the Conservation of Natural Environment from Toxic Ionic Liquids by QSAR Model

2021 ◽  
Vol 287 ◽  
pp. 03007
Author(s):  
Muhammad Ishaq Khan ◽  
Dzulkarnain Zaini ◽  
Azmi Mohd Shariff
Keyword(s):  
Ionic Liquids ◽  
Natural Environment ◽  
Daily Life ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Good Choice ◽  
Ionic Structure ◽  
Quantitative Structure ◽  
Imidazolium Ionic Liquids ◽  
Structure Activity

The natural environment has been affected by human activities to fulfil daily life needs. Abundance and hazardousness of the chemicals including ionic liquids is one of the most challenging aspect to be handled by human as well as for the natural environment. Due to ionic structure, ionic liquids are very good choice for a variety of applications. The natural environment might be affected by the ionic liquids which can be toxic. Therefore, there is a need to address this problem by studying the ecotoxicological behaviour of these ionic liquids. The main objective of current research is to model the toxicity ecotoxicological behaviour is studied by quantitative structure activity relationship (QSAR). QSARs predicts the toxicity of ionic liquids. In current research a relationship between polarizability and toxicity for imidazolium ionic liquids with different alky chain length having NTF2 anion has been modelled. The success of current research will be very helpful to protect the nature by minimizing the killing of testing animals as well as ensuring the safety of biotic components of the ecosystem.

scholarly journals 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Manman Zhao ◽  
Lin Wang ◽  
Linfeng Zheng ◽  
Mengying Zhang ◽  
Chun Qiu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Egfr Inhibitors ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
2D Qsar ◽  
Structure Activity

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2=0.565 (cross-validated correlation coefficient) and r2=0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.

scholarly journals QSAR Study of Indolylisoxazoline Analogues for Their Antiprostat Activity

2019 ◽  
Vol 1 (2) ◽  
pp. 66-71
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molecular Mechanics ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Relationship Study

ABSTRACT Quantitative Structure-Activity relationship study of 13 Indolylisoxazoline analogues as antiprostat agent using multiple linear regression (MLR) has been done. Geometri optimization of Indolylisoxazoline analogues using molecular mechanics (MM+) method. The best QSAR model from regression analysis is log IC50 Pred = 30,877 - 71,847 x qC10 + 165,070 x qC11 + 70,106 x qC13 with most influents descriptor to antiprostat activity are qC10, qC11 dan qC13.

Sign in / Sign up

Export Citation Format

Share Document