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Experimental Determination of Octanol-Water Partition Coefficients of Selected Natural Toxins

preprint
submitted on 29.11.2019, 15:34 and posted on 09.12.2019, 09:39 by Carina Schoensee, Thomas Bucheli
Natural toxins are ubiquitously occurring highly diverse organic compounds produced by e.g., plants or fungi. In predictive environmental fate and risk assessment of organic chemicals for regulatory purposes, the octanol-water partition coefficient (Kow) remains one of the key parameters. However, experimental data for natural toxins is largely missing and current estimation models for Kow show limited applicability for multifunctional, ionizable compounds. Thus, log Kow data was first experimentally derived for a diverse set of 45 largely ionizable natural toxins and then compared to predicted values from three different models (KOWWIN, ACD/Percepta, Chemicalize). Both approaches were critically evaluated with regards to their applicability for multifunctional, ionizable compounds. The miniaturized shake-flask approach allowed reliable quantification of pH dependent partitioning behavior for neutral, acidic and basic ionizable natural toxins. All analyzed toxins are rather polar with an average log Kow < 1 and an observed maximum log Kow of 2.7. Furthermore, the comparison of experimental data to those of commonly used prediction models showed that the latter match the former with only minorly increased errors. The Chemicalize tool gave overall best predictions with a mean absolute error of 0.49 and thus should be preferred in comparison to ACD/Percepta and KOWWIN.

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 722493 (NaToxAq).

History

Email Address of Submitting Author

carina.schoensee@agroscope.admin.ch

Institution

Agroscope

Country

Switzerland

ORCID For Submitting Author

0000-0002-5083-8712

Declaration of Conflict of Interest

No conflict of interest.

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in Journal of Chemical & Engineering Data

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