scholarly journals Prediction of Terpenoids Toxicity Based on Quantitative Structure-Activity Relationships Model

2019 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Chemical Structure ◽  
Vibrio Fischeri ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Geranic Acid ◽  
Structure Activity ◽  
Coefficient Corrélation ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13) and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites which chemical structure is highly diverse. A quantitative structure-activity relationship (QSAR) model to predict the terpenoids toxicity and to evaluate the influences of their chemical structure, was developed in this study, by assessing the toxicity of 27 terpenoid standards using Gram-negative bioluminescent Vibrio fischeri, in real time. Under the test conditions, at concentration of 1 µM, the terpenoids showed a toxicity level lower than five %, with exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30 % at concentration of 50 to 100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene and linalool oxide. Regarding the functional group, the terpenoids toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. CODESSA software was employed to develop the QSAR models based on the correlation of terpenoids toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoids toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., max partial charge for a C atom [Zefirov's PC]) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with training and test coefficient correlation higher than 0.810 and 0.535, respectively, and square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are a suitable and a rapid tool to predict the terpenoids toxicity in a diversity of food products.

scholarly journals Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 628 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Vibrio Fischeri ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Partial Charge ◽  
Quantitative Structure ◽  
Chemical Structures ◽  
Structure Activity ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.

scholarly journals Synthesis, Cytotoxic Activity Evaluation and Quantitative Structure-ActivityAnalysis of Substituted 5,8-Dihydroxy-1,4-naphthoquinones and Their O- and S-Glycoside Derivatives Tested against Neuro-2a Cancer Cells

Marine Drugs ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. 602
Author(s):  
Sergey Polonik ◽  
Galina Likhatskaya ◽  
Yuri Sabutski ◽  
Dmitry Pelageev ◽  
Vladimir Denisenko ◽  
...  
Keyword(s):  
Cytotoxic Activity ◽  
Sea Urchins ◽  
Neuroblastoma Cells ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Mtt Method ◽  
Structure Activity ◽  
Cytotoxic Compounds ◽  
Qsar Models

Based on 6,7-substituted 2,5,8-trihydroxy-1,4-naphtoquinones (1,4-NQs) derived from sea urchins, five new acetyl-O-glucosides of NQs were prepared. A new method of conjugation of per-O-acetylated 1-mercaptosaccharides with 2-hydroxy-1,4-NQs through a methylene spacer was developed. Methylation of 2-hydroxy group of quinone core of acetylthiomethylglycosides by diazomethane and deacetylation of sugar moiety led to 28 new thiomethylglycosidesof 2-hydroxy- and 2-methoxy-1,4-NQs. The cytotoxic activity of starting 1,4-NQs (13 compounds) and their O- and S-glycoside derivatives (37 compounds) was determined by the MTT method against Neuro-2a mouse neuroblastoma cells. Cytotoxic compounds with EC50 = 2.7–87.0 μM and nontoxic compounds with EC50 > 100 μM were found. Acetylated O- and S-glycosides 1,4-NQs were the most potent, with EC50 = 2.7–16.4 μM. Methylation of the 2-OH group innaphthoquinone core led to a sharp increase in the cytotoxic activity of acetylated thioglycosidesof NQs, which was partially retained for their deacetylated derivatives. Thiomethylglycosides of 2-hydroxy-1,4-NQs with OH and MeO groups in quinone core at positions 6 and 7, resprectively formed a nontoxic set of compounds with EC50 > 100 μM. A quantitative structure-activity relationship (QSAR) model of cytotoxic activity of 22 1,4-NQ derivatives was constructed and tested. Descriptors related to the cytotoxic activity of new 1,4-NQ derivatives were determined. The QSAR model is good at predicting the activity of 1,4-NQ derivatives which are unused for QSAR models and nontoxic derivatives.

Descriptor Selection Improvements for Quantitative Structure-Activity Relationships

2019 ◽  
Vol 29 (09) ◽  
pp. 1950016 ◽  
Author(s):  
Liang-Yong Xia ◽  
Qing-Yong Wang ◽  
Zehong Cao ◽  
Yong Liang
Keyword(s):  
Molecular Descriptor ◽  
Area Under The Curve ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Small Subset ◽  
Quantitative Structure ◽  
Complex Samples ◽  
Structure Activity ◽  
Descriptor Selection ◽  
Qsar Models

Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure–activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and [Formula: see text]-values.

scholarly journals Design of New Potent Insecticides of Organophosphate Derivatives Based on QSAR Analysis

2013 ◽  
Vol 13 (1) ◽  
pp. 86-93 ◽  
Author(s):  
Mudasir Mudasir ◽  
Yari Mukti Wibowo ◽  
Harno Dwi Pranowo
Keyword(s):  
Sulfonic Acid ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Benzene Sulfonic Acid ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Qsar Models ◽  
New Compounds

Design of new potent insecticide compounds of organophosphate derivatives based on QSAR (Quantitative Structure-Activity Relationship) analytical model has been conducted. Organophosphate derivative compounds and their activities were obtained from the literature. Computational modeling of the structure of organophosphate derivative compounds and calculation of their QSAR descriptors have been done by AM1 (Austin Model 1) method. The best QSAR model was selected from the QSAR models that used only electronic descriptors and from those using both electronic and molecular descriptors. The best QSAR model obtained was:Log LD50 = 50.872 - 66.457 qC1 - 65.735 qC6 + 83.115 qO7 (n = 30, r = 0.876, adjusted r2 = 0.741, Fcal/Ftab = 9.636, PRESS = 2.414 x 10-6)The best QSAR model was then used to design in silico new compounds of insecticide of organophosphate derivatives with better activity as compared to the existing synthesized organophosphate derivatives. So far, the most potent insecticide of organophosphate compound that has been successfully synthesized had log LD50 of -5.20, while the new designed compound based on the best QSAR model, i.e.: 4-(diethoxy phosphoryloxy) benzene sulfonic acid, had log LD50 prediction of -7.29. Therefore, the new designed insecticide compound is suggested to be synthesized and tested for its activity in laboratory for further verification.

scholarly journals Ezqsar: An R Package for Developing QSAR Models Directly From Structures

2017 ◽  
Vol 11 (1) ◽  
pp. 212-221 ◽  
Author(s):  
Jamal Shamsara
Keyword(s):  
Statistical Parameter ◽  
R Package ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Data Set ◽  
Structure Activity ◽  
Qsar Models ◽  
Test Sets ◽  
Highly Correlated

Background:Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.Method and Materials:Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.Results:Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example.Conclusion:The R package, ezqsar, is freely availableviahttps://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.

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.

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