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Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis

The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coeff...

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Published in:Chemosphere (Oxford) 2016-06, Vol.152, p.190-195
Main Authors: Freitas, Mirlaine R., Barigye, Stephen J., Daré, Joyce K., Freitas, Matheus P.
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Language:English
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description The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r2 = 0.79, q2 = 0.70 and rtest2 = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 − 0.00233 × X9774 − 0.00070 × X813 − 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides. [Display omitted] •Image-based descriptors describe the bioconcentration factors of carbonyl herbicides.•A three-parameter linear equation yielded a predictive and validated QSPR model.•Interpretation of built model provided information on structural features affecting BCF.
doi_str_mv 10.1016/j.chemosphere.2016.03.011
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While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r2 = 0.79, q2 = 0.70 and rtest2 = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 − 0.00233 × X9774 − 0.00070 × X813 − 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides. 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subjects 1-Octanol - chemistry
Bioconcentration
Carbonyl herbicides
Environmental Monitoring - methods
Herbicides - analysis
Herbicides - chemistry
Image analysis
Models, Theoretical
Multivariate Analysis
Prospective Studies
QSPR
Quantitative Structure-Activity Relationship
Solubility
Water - chemistry
Water Pollutants, Chemical - analysis
Water Pollutants, Chemical - chemistry
title Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis
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