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Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0002-7659-8526
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0003-4240-513x
Univ Sci & Technol Beijing, Dept Biol Sci & Engn, Sch Chem & Biol Engn, Beijing, Peoples R China.
COSMOl GmbH & Co KG, Leverkusen, Germany; Univ Regensburg, Inst Phys & Theoret Chem, Regensburg, Germany.
2019 (English)In: ACS Omega, E-ISSN 2470-1343, Vol. 4, no 9, p. 13772-13781Article in journal (Refereed) Published
Abstract [en]

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 kOW) and hexadecane–water (log kHW) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log kOW and log kHW 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 kOW) values were retrieved from the PubChem database, and KowWin log kOW values were determined as well. For 24 of these compounds, log kOW 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 kOW, 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 kHW values are found to be strongly correlated to log kOW for most compounds.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2019. Vol. 4, no 9, p. 13772-13781
National Category
Theoretical Chemistry
Research subject
Chemistry with Specialisation in Theoretical Chemistry
Identifiers
URN: urn:nbn:se:uu:diva-391706DOI: 10.1021/acsomega.9b01277ISI: 000485168200017PubMedID: 31497695OAI: oai:DiVA.org:uu-391706DiVA, id: diva2:1345601
Funder
Swedish Research Council, SNIC2017-12-41Available from: 2019-08-26 Created: 2019-08-26 Last updated: 2023-10-30Bibliographically approved
In thesis
1. Computational Ecotoxicology
Open this publication in new window or tab >>Computational Ecotoxicology
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Human society has progressed by polluting ecosystems since at least the early industrial revolution. Large amounts of harmful chemical compounds have been dispersed in soils, seas, ground waters and wildlife habitats by industrial and anthropomorphic activities over the last two centuries, leading to a persistent toxicological load on the environment. Pollution is a threat to biodiversity, to the health of ecosystems, and to all living organisms. Advances in environmental sciences are needed so that pollutants can be distinguished from harmless compounds. New methods could ease the enormous task of sorting out hazardous chemicals, and also facilitate the study of existing problems in ecotoxicology, which are often hampered by insufficient data. In our research, we apply the methods of computational chemistry to predict the interactions of various toxins, carcinogens, nanoparticles and xenobiotics with proteins, DNA, and cell membranes. Methods such as molecular dynamics simulations, docking, and quantum chemistry are at the core of these studies, each having its role in facilitating the enormous task of transforming in vitro ecotoxicology to in silico ecotoxicology. We perform detailed studies of a few compounds and receptors, as well as larger, more comprehensive groups of compounds. We also outline approaches for drawing computational conclusions about the molecular behaviour of various potential environmental toxins by modelling their interactions with DNA and proteins, and we use partition coefficients to describe their ability to permeate the cell membrane. Methods for studying the purification of pollutants from essential sources, such as water, are proposed. We also investigate the emerging problem of nanoparticle pollution and propose computational approaches to model the formation of nanoparticles from combustion emissions and the interactions of such particles with atmospheric components.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2022. p. 73
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1966
Keywords
pollutants, simulation, molecular, dynamic, quantum, chemistry
National Category
Biophysics Structural Biology Bioinformatics and Computational Biology
Research subject
Biology with specialization in Molecular Biotechnology
Identifiers
urn:nbn:se:uu:diva-419868 (URN)978-91-513-1485-3 (ISBN)
Public defence
2022-06-08, A1:111a, BMC, Husargatan 3, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2022-05-18 Created: 2020-09-17 Last updated: 2025-02-20Bibliographically approved

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