AI Article Synopsis
Article Abstract
In this work, quantitative structure-activity relationships (QSARs) were developed to aid human and environmental risk assessment processes for brominated flame retardants (BFRs). Brominated flame retardants, such as the high-production-volume chemicals polybrominated diphenyl ethers (PBDEs), tetrabromobisphenol A, and hexabromocyclododecane, have been identified as potential endocrine disruptors. Quantitative structure-activity relationship models were built based on the in vitro potencies of 26 selected BFRs. The in vitro assays included interactions with, for example, androgen, progesterone, estrogen, and dioxin (aryl hydrocarbon) receptor, plus competition with thyroxine for its plasma carrier protein (transthyretin), inhibition of estradiol sulfation via sulfotransferase, and finally, rate of metabolization. The QSAR modeling, a number of physicochemical parameters were calculated describing the electronic, lipophilic, and structural characteristics of the molecules. These include frontier molecular orbitals, molecular charges, polarities, log octanol/water partitioning coefficient, and two- and three-dimensional molecularproperties. Experimental properties were included and measured for PBDEs, such as their individual ultraviolet spectra (200-320 nm) and retention times on three different high-performance liquid chromatography columns and one nonpolar gas chromatography column. Quantitative structure-activity relationship models based on androgen antagonism and metabolic degradation rates generally gave similar results, suggesting that lower-brominated PBDEs with bromine substitutions in ortho positions and bromine-free meta- and para positions had the highest potencies and metabolic degradation rates. Predictions made for the constituents of the technical flame retardant Bromkal 70-5DE found BDE 17 to be a potent androgen antagonist and BDE 66, which is a relevant PBDE in environmental samples, to be only a weak antagonist.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1897/06-308r.1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Electron transfer tuning for persulfate activation via the radical and non-radical pathways with biochar mediator.
J Hazard Mater
December 2024
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu, Sichuan 610059, P.R. China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P.R. China. Electronic address:
Electron mediator-based in-situ chemical oxidation (ISCO) offers a novel strategy for groundwater remediation due to diverse reaction pathways. However, distinguishing and further tuning the reaction pathway remains challenging. Herein, biochar as an electron mediator targeted active peroxysulphate (PDS) via the radical or non-radical pathway.
View Article and Find Full Text PDFBase-catalyzed hydrolysis mechanism of polychlorinated dibenzo-p-dioxins based on quantitative structure-activity relationship.
Chemosphere
December 2024
College of Environmental Science and Engineering, Liaoning Technical University, Fuxin 123000, China.
Polychlorinated dibenzo-p-dioxins (PCDDs) are persistent organic pollutants that pose considerable threats to ecological and human health owing to their high toxicity potential. Understanding the mechanisms for underlying the base-catalyzed hydrolysis of PCDDs in aquatic environments is essential for assessing their environmental behaviour and ecological risks. Herein, we combined quantitative structure-activity relationship (QSAR) models with density functional theory calculations to analyse the base-catalyzed hydrolysis mechanisms of PCDDs.
View Article and Find Full Text PDFToxicity assessment and i-QSTTR analysis of ionic liquids on D. magna, D. rerio, and R. subcapitata.
Sci Total Environ
December 2024
School of Pharmacy, Lanzhou University, Lanzhou 730000, China; Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China. Electronic address:
The study aimed to assess the impacts of ionic liquids (ILs) as innovative alternatives to traditional organic solvents on aquatic environments and human health. Five machine learning methods, including multiple linear regression (MLR), partial least squares regression (PLS), random forest regression (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), were used to construct the prediction models of the toxicity of ILs to D. magna, D.
View Article and Find Full Text PDFHepatotoxicity, developmental toxicity, and neurotoxicity risks associated with co-exposure of zebrafish to fluoroquinolone antibiotics and tire microplastics: An in silico study.
J Hazard Mater
December 2024
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China. Electronic address:
This study aimed to investigate the differences in the mechanisms of microscopic hepatotoxicity, developmental toxicity, and neurotoxicity in aquatic organisms co-exposed to styrene-butadiene rubber tire microplastics (SBR TMPs) and fluoroquinolone antibiotics (FQs). We found that hepatotoxicity in zebrafish induced by SBR TMPs and FQs was significantly higher than developmental toxicity and neurotoxicity. Furthermore, the main effects of the FQs primarily manifested as synergistic toxicity, whereas the low- and high-order interactions of the FQs mainly exhibited synergistic and antagonistic effects, respectively.
View Article and Find Full Text PDFUtilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study.
BMC Chem
December 2024
Department of Biology, Pharmaceutical Biology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Staudtstr. 5, 91058, Erlangen, Germany.
In virtual drug screening, consensus docking is a standard in-silico approach consisting of a combined result from optimized docking experiments, a minimum of two results combination. Therefore, consensus docking is subjected to a lower success rate than the best docking method due to its mathematical nature, an unavoidable limitation. This study aims to overcome this drawback via random forest, an ensemble machine learning model.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!