Ecotoxicological Impact Assessment of the Production of Cotton Fabric

2020 ◽  
Vol 7 (6) ◽  
pp. 23-32
Author(s):  
Jiahong Qian ◽  
Yuying Qiu ◽  
Xiang Ji ◽  
Yiduo Yang ◽  
Laili Wang
Keyword(s):  
Cotton Fabric ◽  
Primary Alcohol ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Woven Fabric ◽  
Alcohol Ethoxylate ◽  
Characterization Factors ◽  
Chemical Pollutants ◽  
Fabric Production ◽  
Abundant Data

Textiles and garments are increasingly being included in life cycle assessment (LCA) studies because the use of chemicals in industrial production of these items has potential environmental impacts. The USEtox model, characterized by ecotoxicity characterization factors based on abundant data, is a useful tool for assessing the toxicity of chemical pollutants. The objectives of this study were to estimate characterization factors of cotton fabric-related chemicals based on data from a quantitative structure–activity relationship (QSAR) model and assess the ecotoxicological impact of cotton woven fabric. The research boundary ranged from fabric production to wet treatment. Wet treatment was found to contribute more to ecotoxicity than fabric production did, with primary alcohol ethoxylate and sodium hydroxide being the main pollutants.

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2

2020 ◽  
Vol 23 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  
Keyword(s):  
Histone Deacetylases ◽  
Rational Design ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Sirtuin 1 ◽  
Data Set ◽  
Functional Roles ◽  
Molecular Features ◽  
Oecd Principles ◽  
Qsar Models

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

scholarly journals Flavonoid Glycosides with a Triazole Moiety for Marine Antifouling Applications: Synthesis and Biological Activity Evaluation

Marine Drugs ◽  
2020 ◽  
Vol 19 (1) ◽  
pp. 5
Author(s):  
Daniela Pereira ◽  
Catarina Gonçalves ◽  
Beatriz T. Martins ◽  
Andreia Palmeira ◽  
Vitor Vasconcelos ◽  
...  
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Artemia Salina ◽  
Flavonoid Glycosides ◽  
Antifouling Activity ◽  
Antifouling Coatings ◽  
Marine Industry ◽  
Low Toxicity ◽  
Polymethoxylated Flavones ◽  
First Time

Over the last decades, antifouling coatings containing biocidal compounds as active ingredients were used to prevent biofouling, and eco-friendly alternatives are needed. Previous research from our group showed that polymethoxylated chalcones and glycosylated flavones obtained by synthesis displayed antifouling activity with low toxicity. In this work, ten new polymethoxylated flavones and chalcones were synthesized for the first time, including eight with a triazole moiety. Eight known flavones and chalcones were also synthesized and tested in order to construct a quantitative structure-activity relationship (QSAR) model for these compounds. Three different antifouling profiles were found: three compounds (1b, 11a and 11b) exhibited anti-settlement activity against a macrofouling species (Mytilus galloprovincialis), two compounds (6a and 6b) exhibited inhibitory activity against the biofilm-forming marine bacteria Roseobacter litoralis and one compound (7b) exhibited activity against both mussel larvae and microalgae Navicula sp. Hydrogen bonding acceptor ability of the molecule was the most significant descriptor contributing positively to the mussel larvae anti-settlement activity and, in fact, the triazolyl glycosylated chalcone 7b was the most potent compound against this species. The most promising compounds were not toxic to Artemia salina, highlighting the importance of pursuing the development of new synthetic antifouling agents as an ecofriendly and sustainable alternative for the marine industry.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Antimicrobial Screening, in Silico Studies and QSAR of Chalcone-based 1,4-disubstituted 1,2,3-triazole Hybrids

Drug Research ◽  
2020 ◽  
Author(s):  
Pinki Yadav ◽  
Kashmiri Lal ◽  
Ashwani Kumar
Keyword(s):  
Topoisomerase Ii ◽  
Antimicrobial Properties ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Binding Modes ◽  
E Coli ◽  
Structure Activity ◽  
In Silico Studies ◽  
Microbial Strains

AbstractThe in vitro antimicrobial properties of some chalcones (1a–1c ) and chalcone tethred 1,4-disubstituted 1,2,3-triazoles (2a–2u) towards different microbial strains viz. Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger and Candida albicans are reported. Compounds 2g and 2u exhibited better potency than the standard Fluconazole with MIC values of 0.0063 µmol/mL and 0.0068 µmol/mL, respectively. Furthermore, molecular docking was performed to investigate the binding modes of two potent compounds 2q and 2g with E. coli topoisomerase II DNA gyrase B and C. albicans lanosterol 14α-demethylase, respectively. Based on these results, a statistically significant quantitative structure activity relationship (QSAR) model was successfully summarized for antibacterial activity against B. subtilis.

scholarly journals Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 174 ◽  
Author(s):  
Giang Huong Ta ◽  
Cin-Syong Jhang ◽  
Ching-Feng Weng ◽  
Max K. Leong
Keyword(s):  
Drug Discovery ◽  
Support Vector Regression ◽  
Human Colon ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Human Colon Carcinoma Cell ◽  
Drug Discovery And Development ◽  
Training Samples ◽  
Experimental Values

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

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 Pemberdayaan Kelompok Tenun Nari-Nari Melalui Pewarnaan Benang Menggunakan Ekstrak Kayu Kuning Dan Kayu Mahoni

2020 ◽  
Vol 5 (2) ◽  
pp. 446-452
Author(s):  
Ruslan R ◽  
Agrippina Wiraningtyas ◽  
Ahmad Sandi ◽  
Muhammad Nasir
Keyword(s):  
Natural Dyes ◽  
Raw Material ◽  
Woven Fabric ◽  
Synthetic Dyes ◽  
Community Group ◽  
Training Stage ◽  
Red Color ◽  
Different Color ◽  
Fabric Production ◽  
The Cost

The "Nari-Nari" Weaving Village in Rabadompu Timur Village, Bima City, is a community group engaged in the weaving industry which has been carried on for generations. During this time, Bima woven fabric products use yarn raw material that has been colored using synthetic dyes. Yarn with synthetic dyes has a more diverse color, the fabric coloring process is easier and the cost is cheap, but synthetic dyes are carcinogenic and harmful to the environment. The solution to the problems faced by using natural dyes obtained from plants. This activity aims to train the Nari-Nari weaving group in yarn coloring using natural dyes. The method used is training through several stages of the activity namely the stage of socialization of activities; the training stage of yarn dyeing and woven fabric production. The dyes used are yellow wood extract and mahogany wood. The results obtained in this activity are the colored yarn has a different color based on the extract of the dye and fixation material. In yellow wood obtained with a maroon red color on alum, black on tunjung and reddish beige on lime. In mahogany wood is obtained beige on alum, black gray on tunjung and beige on lime.  

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