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Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

[Image: see text] 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 o...

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Autores principales: van der Spoel, David, Manzetti, Sergio, Zhang, Haiyang, Klamt, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713992/
https://www.ncbi.nlm.nih.gov/pubmed/31497695
http://dx.doi.org/10.1021/acsomega.9b01277
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author van der Spoel, David
Manzetti, Sergio
Zhang, Haiyang
Klamt, Andreas
author_facet van der Spoel, David
Manzetti, Sergio
Zhang, Haiyang
Klamt, Andreas
author_sort van der Spoel, David
collection PubMed
description [Image: see text] 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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spelling pubmed-67139922019-09-06 Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods van der Spoel, David Manzetti, Sergio Zhang, Haiyang Klamt, Andreas ACS Omega [Image: see text] 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. American Chemical Society 2019-08-12 /pmc/articles/PMC6713992/ /pubmed/31497695 http://dx.doi.org/10.1021/acsomega.9b01277 Text en Copyright © 2019 American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.
spellingShingle van der Spoel, David
Manzetti, Sergio
Zhang, Haiyang
Klamt, Andreas
Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
title Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
title_full Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
title_fullStr Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
title_full_unstemmed Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
title_short Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
title_sort prediction of partition coefficients of environmental toxins using computational chemistry methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713992/
https://www.ncbi.nlm.nih.gov/pubmed/31497695
http://dx.doi.org/10.1021/acsomega.9b01277
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