Prediction of Phospholipid–Water Partition Coefficients of Ionic Organic Chemicals Using the Mechanistic Model COSMOmic
| Autor: | Andreas Klamt, Kai-Uwe Goss, Larissa Pohler, Simon Spycher, Kai Bittermann, Uwe Huniar, Satoshi Endo |
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| Rok vydání: | 2014 |
| Předmět: |
Ions
Work (thermodynamics) Organic chemicals Phospholipid Water Thermodynamics Ionic bonding Membrane Potentials Surfaces Coatings and Films Organic molecules Partition coefficient Dipole chemistry.chemical_compound Membrane Models Chemical chemistry Materials Chemistry Organic chemistry Organic Chemicals Physical and Theoretical Chemistry Phospholipids |
| Zdroj: | The Journal of Physical Chemistry B. 118:14833-14842 |
| ISSN: | 1520-5207 1520-6106 |
| DOI: | 10.1021/jp509348a |
| Popis: | The partition coefficient of chemicals from water to phospholipid membrane, K(lipw), is of central importance for various fields. For neutral organic molecules, log K(lipw) correlates with the log of bulk solvent-water partition coefficients such as the octanol-water partition coefficient. However, this is not the case for charged compounds, for which a mechanistic modeling approach is highly necessary. In this work, we extend the model COSMOmic, which adapts the COSMO-RS theory for anisotropic phases and has been shown to reliably predict K(lipw) for neutral compounds, to the use of ionic compounds. To make the COSMOmic model applicable for ionic solutes, we implemented the internal membrane dipole potential in COSMOmic. We empirically optimized the potential with experimental K(lipw) data of 161 neutral and 75 ionic compounds, yielding potential shapes that agree well with experimentally determined potentials from the literature. This model refinement has no negative effect on the prediction accuracy of neutral compounds (root-mean-square error, RMSE = 0.62 log units), while it highly improves the prediction of ions (RMSE = 0.70 log units). The refined COSMOmic is, to our knowledge, the first mechanistic model that predicts K(lipw) of both ionic and neutral species with accuracies better than 1 log unit. |
| Databáze: | OpenAIRE |
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