The effect of different log P algorithms on the modeling of the soil sorption coefficient of nonionic pesticides

Reis, Ralpho Rinaldo dos; Sampaio, Sílvio César; Melo, Eduardo Borges de

doi:10.1016/j.watres.2013.06.053

Cited by 17 publications

(10 citation statements)

References 33 publications

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“…109 More recently, dos Reis and co-workers compared different prediction algorithms in a statistical analysis and found KowWin to be one of the most accurate ones. 111 In contrast, Geisler et al, in a comparison of log k OW prediction for small compounds, found KowWin to be quite a bit less accurate than COSMOtherm, 112 which is more in line with the results presented here. It is of interest that development of QSPR methods is ongoing, 114 in part fueled by the finding that databases used to derive older QSPR methods from needed to be curated.…”

Section: Discussionsupporting

confidence: 87%

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

et al. 2019

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The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol–water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol–water (log k OW ) and hexadecane–water (log k HW ) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log k OW and log k HW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree–Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log k OW ) values were retrieved from the PubChem database, and KowWin log k OW values were determined as well. For 24 of these compounds, log k OW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log k OW , and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log k HW values are found to be strongly correlated to log k OW for most compounds.

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Section: Discussionsupporting

confidence: 87%

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

et al. 2019

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“…The selected physicochemical properties and molecular descriptors are consistent with the results of previous studies. The sorption of nonionic organic compounds in soil is related to a mechanism that makes hydrophobicity the driving force (Wen et al 2012;dos Reis et al 2013dos Reis et al , 2014. Thus, the physicochemical properties and molecular descriptors related to the hydrophobicity of the pesticides are relevant for the adsorption process.…”

Section: Results Of Gbdt Modelmentioning

confidence: 99%

“…Specifically, previous studies indicated that log P is closely related to the prediction of K OC . A model using predicted values of log P was developed by dos Reis et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models

Kobayashi

Uchida

Yoshida

2020

Enviro Toxic and Chemistry

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The soil adsorption coefficient (KOC) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to study and obtain the KOC value of a pesticide. Furthermore, acquiring the KOC requires a large cost. It is necessary to efficiently estimate the KOC value in the early stages of pesticide development. In the present study, the experimental values of physicochemical properties and molecular descriptors of chemical structures were collected to develop a quantitative structure–property relationship (QSPR) model, and the prediction performance of the model was evaluated. More specifically, we compared the accuracies of models based on a gradient boosting decision tree, multiple linear regression, and support vector machine. The experimental results suggest that it is possible to develop a QSPR model with high accuracy using both the molecular descriptors calculated from the structural formula and experimental values of physicochemical properties from open literature and databases. Comparing to the previously established models, we achieved high prediction accuracy, fitness, and robustness by only using freeware. Therefore, our developed QSPR models can be useful preliminary risk assessment in the early developmental stages of pesticides. Environ Toxicol Chem 2020;39:1451–1459. © 2020 SETAC

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“…However, the use of wastewater in large doses may lead to the accumulation of residues in soils (LUCAS et al, 2013), percolated water, leading to the leaching of ions (PRIOR et al, 2009;MAGGI et al, 2011), and in runoff material (WANG et al, 2013), contaminating water resources. Thus, researches on residue physical and chemical properties are essential for planning actions that allow recovering contaminated areas or preserving those non-degraded (DOS REIS et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Soil Meso- And Macrofauna in Two Soybean Crops After Swine Wastewater Application

et al. 2017

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Hog raising generates a large amount of residues that is commonly discarded into the soil as fertilizer even with environmental risks. In this context, the aim of this study was to assess the application effects of different doses of swine wastewater (SW) associated with mineral fertilization on the abundance and diversity of organisms of soil meso-and macrofauna in two soybean crops. Treatments consisted of four doses (0, 100, 200, and 300 m 3 ha −1 ) of SW with or without mineral fertilization in two soybean crop, totaling 24 experimental units. Soil meso-and macrofauna were sampled using pitfall traps installed at each plot. Samples were sorted and the organisms were separated and identified. The highest abundances were found between groups of springtails and spider mites. Soil meso-and macrofauna differed between soybean crops influenced by soil physical and chemical parameters. Doses of swine wastewater between 0 and 300 m 3 ha −1and mineral fertilization do not have effects on ecological indices of soil meso-and macrofauna. However, periodic applications over time change soil physicochemical variables, which may lead to negative effects in the long term.

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The effect of different log P algorithms on the modeling of the soil sorption coefficient of nonionic pesticides

Cited by 17 publications

References 33 publications

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models

Soil Meso- And Macrofauna in Two Soybean Crops After Swine Wastewater Application

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