QSAR of Acute Toxicity of Halogenated Phenol to Green Fluorescent Protein by Using Density Functional Theory

Liu, Xiao Xia; Li, J. Y.; Yu, Jianguo; Sun, Sh. Q.; Wang, Y. J.; Liu, H. X.

doi:10.1007/s00128-012-0819-0

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…In silico computational methods currently used in predictive toxicology include read-across, physiologically based pharmacokinetic (PBPK), and pharmacokinetics and pharmacodynamics (PKPD) model, and quantitative structure–activity relationship (QSAR), among others. ,, Among the in silico toxicity prediction models, QSAR is one of the most widely used methods. Traditional QSAR models have been successfully applied to predict toxicity endpoints using the physio-chemical properties-based descriptors such as two-dimensional (2D) and three-dimensional (3D) topological, geometric, electronic, and polar properties of the chemicals. − To improve the predictive power, experimentally derived physiochemical descriptors are also being augmented in the QSAR model in conjunction with the computationally derived chemical descriptors . However, traditional QSAR models suffer from certain limitations including limited applicability domain, as they are often suitable only for chemicals with similar structures and lack of mechanistic understanding of the association between the toxicity mechanism and the chemical descriptors.…”

Section: Introductionmentioning

confidence: 99%

In Vitro Biodescriptors Derived from Time Series Toxicogenomics Data in In Silico QSAR Improve the Phenotypic Toxicity Prediction

Rahman,

Lan,

Gou

et al. 2023

ACS EST Water

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In this study, we explored the bioassay-based Quantitative Biological Activity Relationship (QBAR) model with in vitro high-throughput screening-based temporal transcriptomic and proteomic data of known stress response pathways, as the biological descriptors, to predict in vivo toxicity. We, for the first time, constructed and compared QBAR models with quantitative biodescriptors derived at three biologically relevant levels�individual biomarker, specific pathway, and whole cellular level. For all the models, two separate case studies were conducted including cytotoxicity (Escherichia coli half-maximal effective concentration) prediction from an E. coli-based transcriptomic assay and genotoxicity (in vivo carcinogenicity) prediction from a yeast-based proteomic assay. For both case studies, QBAR models using biodescriptors perform better (with higher R 2 and accuracy values) than the conventional QSAR models or the Quantitative Structure and Biological Activity Relationship (QSBAR) models with combined physical-chemical and biological descriptors. Among the three biological levels for biodescriptors derivation, the QBAR model with descriptors indicative of the pathway-level molecular disturbance led to the best prediction (R 2 = 0.78 for cytotoxicity prediction; accuracy = 75% for carcinogenicity prediction). In addition, the QBAR model identified top ranked biodescriptors that contributed significantly to the toxicity prediction, thus providing insights into mode of action. Applicability domain analysis reveals that the derived QBAR models can produce reliable predictions using the biological descriptors.

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Section: Introductionmentioning

confidence: 99%

In Vitro Biodescriptors Derived from Time Series Toxicogenomics Data in In Silico QSAR Improve the Phenotypic Toxicity Prediction

Rahman,

Lan,

Gou

et al. 2023

ACS EST Water

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“…For example, Liu etc. [3] established a novel approach to predict toxicity of 14 halogenated phenols by using green fluorescent protein (GFP) as bio-marker. Mo etc.…”

Section: Introductionmentioning

confidence: 99%

QSAR Analyzes for the Predictive Toxicity of Substituted Phenols and Anilines to Fish (<i>carp</i>)

Gao

Liu

Chen

2013

AMM

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A quantitative structure-activity relationship (QSAR) study for predicting the acute toxicity 96h - LC50 values of substituted anilines and phenols to carp is presented in this work. For this, the descriptors were obtained with DFT method at the B3LYP/6-311G** level using the Gaussian 03 software package. Afterwards the obtained parameters were taken as theoretical descriptors to establish a QSAR model for predicting -lgLC50. The model contains two variables, energy of the highest occupied molecular orbital (EHOMO) and energy of the lowest unoccupied molecular orbital (ELUMO), which suggest that the main effect on biological toxicity of phenols and anilines is the interaction of electrons between the molecules of organic chemicals. Besides, the model was further validated by variance inflation factors (VIF) and t-test, and show fine stabilities and predictive abilities, which can be used to predict -lgLC50 of these kinds of compounds.

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Acute oral toxicity and liver oxidant/antioxidant stress of halogenated benzene, phenol, and diphenyl ether in mice: a comparative and mechanism exploration

Shi

Feng

Zhang

et al. 2013

Environ Sci Pollut Res

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The lethal doses (LD50s) of fluorinated, chlorinated, brominated, and iodinated benzene, phenol, and diphenyl ether in mice were ascertained respectively under the consistent condition. The acute toxicity of four benzenes orders in fluorobenzene (FB) < iodobenzene < chlorobenzene≈bromobenzene, that of four phenols orders in 4-iodophenol≈4-bromophenol < 4-chlorophenol (4-MCP) < 4-fluorophenol (4-MFP), and that of four diphenyl ethers orders in 4,4'-iododiphenyl ether < 4,4'-difluorodiphenyl ether < 4,4'-dichlorodiphenyl ether≈4,4'-dibromodiphenyl ether. General behavior adverse effects were observed, and poisoned mouse were dissected to observe visceral lesions. FB, 4-MCP, and 4-MFP produced toxic faster than other halogenated benzenes and phenols, as they had lower octanol-water partition coefficients. Pathological changes in liver and liver/kidney weight changes were also observed. Hepatic superoxide dismutase, catalase activities, and malondialdehyde level were tested after a 28-day exposure, which reflects a toxicity order basically consistent with that reflected by the LD50s. By theoretical calculation and building models, the toxicity of benzene, phenol, and diphenyl ether were influenced by different structural properties.

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QSAR of Acute Toxicity of Halogenated Phenol to Green Fluorescent Protein by Using Density Functional Theory

Cited by 4 publications

References 8 publications

In Vitro Biodescriptors Derived from Time Series Toxicogenomics Data in In Silico QSAR Improve the Phenotypic Toxicity Prediction

In Vitro Biodescriptors Derived from Time Series Toxicogenomics Data in In Silico QSAR Improve the Phenotypic Toxicity Prediction

QSAR Analyzes for the Predictive Toxicity of Substituted Phenols and Anilines to Fish (<i>carp</i>)

Acute oral toxicity and liver oxidant/antioxidant stress of halogenated benzene, phenol, and diphenyl ether in mice: a comparative and mechanism exploration

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