Quantitative structure-activity relationships in fish toxicity studies Part 1: Relationship for 50 industrial pollutants
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Aquatic toxicology
In silico tools are critical to addressing data gaps in aquatic hazard assessment and chemical prioritization. Although quantitative structure–activity relationship (QSAR) models have been used in aquatic toxicology for decades, most models have high uncertainty or limited domains of applicability. This chapter introduces a three-dimensional QSAR approach (3D-QSAR) utilizing a comparative analysis of protein–ligand interactions (CAPLI). The CAPLI approach uses a combination of molecular docking and 3D-pharmacophore to identify the key amino acids responsible for selectivity and potency of chemicals, which allows understanding of differences in species sensitivity.
Prioritization
Aquatic toxicology
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In the basise on the early study, it has been given that one congruity description on the quantitative structure activity/properties relation(QSAR/QSPR).The important is in trodcing the charateristic on study QSAR/QSPR. Meanwhle, it has been look into the future that the developing foreground on study the QSAR/QSPR on compound molecule's.
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A set of 24 halogen containing hydroxy and amino substituted aromatic compounds were subjected to 2D- and 3D-QSAR studies. 3D-QSAR was studied at a 2.0 Ǻ 3D grid spacing using molecular interaction fields (MIFs) analysis. The best predictive models by MIFs gave the cross-validated correlation coefficient, Q2 of 0.668 and squared correlation coefficient, R2 of 0.979 and the models by MLR, PCR and PLSR methods for 2D-QSAR provided a highly significant squared correlation coefficient (R2) values of 0.904, 0.785, 0.903 and cross-validated correlation coefficients (Q2) of 0.824, 0.662 and 0.718 respectively. The statistically significant model was established from a training set of 18 molecules, which were validated by evaluation of test set of 6 compounds. The calculated cytotoxic activities through MIFs model showed a very good agreement with experimental values. The information provided by QSAR analysis may give valuable clues to design and find the new potential drugs.
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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
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Aquatic toxicology
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Tetrahymena pyriformis
Daphnia magna
Aquatic toxicology
Dihydrofolate reductase
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Previous studies reported on a large (> 80%) compliance between the observed toxicity of pesticide mixtures and their toxicity as predicted by the concept of concentration addition (CA). The present study extents these findings to commercially sold and frequently applied pesticide mixtures by investigating whether the aquatic toxicity of 66 herbicidal and 53 fungicidal combination products, i.e., authorized plant protection products that contain two or more active substances, can reliably be predicted by CA. In more than 50% of cases, the predicted and observed mixture toxicity deviated by less than factor 2. An indication for a synergistic interaction was only detected with regard to algal growth inhibition for mixtures of fungicides that inhibit different enzymes of ergosterol biosynthesis. The greatest degree of compliance between prediction and observation was found for the acute toxicity of fungicidal products towards Daphnia and fish, while the greatest degree of underestimation of product toxicity occurred for the acute toxicity of herbicidal products towards Daphnia and fish. Using the lowest available toxicity measures within taxonomic groups as the most conservative approach resulted in a bias towards overestimation of product toxicity, but did not eliminate cases of considerable underestimation of product toxicity. The results suggest that the CA concept can be applied to predict the aquatic toxicity of commercial pesticide mixtures using the heterogeneous data typically available in a risk assessment context for a number of clearly identified combinations of test species and pesticide types with reasonably small uncertainty.
Daphnia magna
Aquatic toxicology
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This study was carried out elucidate the structural properties required for pyridazinyl derivatives to exhibit angiotensin II receptor activity. The best 2D-QSAR model was selected, having correlation coefficient r2 = 0.8156, cross validated squared correlation coefficient q2 = 0.7348 and predictive ability of the selected model was also confirmed by leave one out cross validation method. Further analysis was carried out using 3D-QSAR method k-nearest neighbor molecular field analysis approach; a leave-one-out crossvalidated correlation coefficient of 0.7188 and a predictivity for the external test set (0.7613) were obtained. By studying the QSAR models, one can select the suitable substituent for active compound with maximum potency.
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Aquatic toxicology
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Effect concentrations for aquatic baseline toxicity generally decrease with increasing log octanol-water partition co-efficient (Kow) values of up to 5 to 6, whereas less is known about the baseline toxicity of organic chemicals with log Kow values above 6. A physicochemical analysis of the dissolution process for organic chemicals was combined with reported baseline toxicity data, leading to the following conclusions. First, no absolute hydrophobicity cutoff exists for baseline toxicity at a log Kow value of 6, because aquatic baseline toxicity for fish and algae was observed for chemicals with log Kow values greater than 6.5 and with effect concentrations less than 10 microg/L. Second, the baseline toxicity of hydrophobic organic substances was exerted at a relatively constant chemical activity of 0.01 to 0.1. Finally, organic chemicals with high melting points cannot provide sufficient chemical activity to exert baseline toxicity when considered as individual, pure chemicals. However, such substances are still expected to contribute to baseline toxicity when part of a complex mixture.
Aquatic toxicology
Baseline (sea)
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Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.
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