Study on the acute toxicity of chemicals to fish by QSAR model
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To manage the use of chemicals,to predict the toxicity of unknown compounds is needed.A series of acute toxicity of environmental chemicals to fish is predicted by Quantitative structure-activity relationship(QSAR) method.The progress in QSAR research,methodology,mathematical modeling and molecular descriptors is reviewed.Finally,the octanol/water partition coefficient method is used to summarize the acute toxicity of 119 compounds to fish,which can provide significant value of predicting the acute toxicity of environmental chemicals.Keywords:
Molecular descriptor
Aquatic toxicology
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Guppy
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Risk assessment and management of chemicals should focus more on integrated toxicities based on multiple species effects. 50 nitroaromatic compounds were studied to evaluate the integrated toxicities of chemicals which lack toxicity data, with the toxicities of some combinations of species also collected. Robust quantitative structure-activity relationship (QSAR) models were developed to predict the missing toxicity data and to explain the toxicity mechanisms of nitroaromatic compounds whose toxicities are mainly determined by their electrophilic reactivity. The principle component analysis (PCA) method was used to calculate integrated toxicity indexes (ITI) of 50 nitroaromatic compounds based on the toxicity data from the QSAR models. A single-parameter QSAR model was then developed to directly predict integrated toxicity indexes from the structure parameters of nitroaromatic compounds. The results show that the combination of QSAR and PCA can successfully evaluate and predict the integrated toxicities of combination of nitroaromatic compounds.
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Aquatic toxicology
Chemical toxicity
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A large amount of overall organic chemical compounds produced and used annually pertain to aromatic compounds, highly toxic to living organisms in aquatic systems and soil, but to humans too, and moreover, many of them are reported as carcinogenic and mutagenic. One of the most successful approaches for predicting their toxic effect could be found in the application of QSAR/QSPR (quantitative structure-activity/property relationship) modeling. This powerful technique quantitatively relates variations in biological activity, i.e. toxicity, to changes in molecular structure and properties. Hence, the goal of the study was to predict toxicity in vivo of aromatic compounds structured by single benzene ring and including presence and absence of different substitute groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc, by using QSAR/QSPR tool. A Genetic Algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. Evaluation of models was performed by calculating and comparing their model performances (R2, s, F, Q2) after splitting set of organic compounds to training and test sets. As the most predictive model is shown the 3-variable model having also a good ratio of the number of descriptors and their predictive ability. The main contribution to the toxicity showed descriptors belonging to 2D autocorrelation and atom-centered fragments descriptors, respectively. The GA-MLRA approach showed good results in this study, which allows to built simple, interpretable and transparent model that can be used for future studies of predicting toxicity of organic compounds to mammals
Molecular descriptor
Applicability domain
Aquatic toxicology
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Abstract A quantitative structure-activity relationship (QSAR) model for the prediction of aquatic toxicity of organic chemicals was formulated. Toxicity data of 394 compounds with a wide variety of structures were collected from a data base and other literature. The molecular structures were quantitatively described, using substructural and hydrophobic parameters for the QSAR analysis. Fuzzy adaptive least-squares (FALS), a pattern recognition method recently developed at our laboratory, was used in the QSAR study. A discriminant function with 37 variables was obtained, representing satisfactorily the correlation of structure with aquatic toxicity ratings of compounds. The results were highly significant both in recognition and in leave-one-out prediction.
Aquatic toxicology
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This paper describes the building of a model by multiple linear regression method,which is based on the quantitative structure-activity relationship,involving the data collected that are related to the quantitative relationship between substituted benzenes and 48 h-LC50 and 96 h-LC50 of zebra fish(Brachydanio rerio),and the characterization of compounds by calculating molecular descriptors with AM1 method. According to the principle of QSAR,evaluation and validation of the model are carried out,and to further verify the model's predictive ability the acute toxicities of 4-Chlorophenol,2,4-dichlorophenol and 2,4,6-trichlorphenol are studied using zebra fish in terms of 48 h-LC50 as well as 96 h-LC50,respectively. By comparing the experimental values of the acute toxicity tests with the predicted values of QSAR model,the residuals all meeting δ1,it is then concluded that the QSAR model has a good predictive ability.
Molecular descriptor
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Aquatic toxicology
Molecular descriptor
Lipophilicity
Octanol
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Pimephales promelas
Aquatic toxicology
Molecular descriptor
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