Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS

Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS Performance of COSMO-RS method as a tool for partition and distribution modeling in 20 solvent pairs—composed of neutral or acidic aqueous solution and organic solvents of different polarity, ranging from alcohols to toluene and hexane—was evaluated. Experimental partition/distribution data of lignin-related and drug-like compounds (neutral, acidic, moderately basic) were used as reference. Several aspects of partition modeling were addressed: accounting for mutual saturation of aqueous and organic phases, variability of systematic prediction errors across solvent pairs, taking solute ionization into account. COSMO-RS was found to predict extraction outcome for both ligneous and drug-like compounds in various solvent pairs fairly well without any additional empirical input. The solvent-specific systematic errors were found to be moderate, despite being statistically significant, and related to the solvent hydrophobicity. Accounting for mutual solubilities of the two liquids was proven crucial in cases where water was considerably soluble in the organic solvent. The root mean square error of a priori logP prediction varied, depending mainly on the solvent pair, from 0.2 to 0.7, overall value being 0.6 log units. The accuracy was higher in case of hydrophilic than hydrophobic solvents. The logD predictions were less accurate, due to pK a prediction being an additional source of error, and also because of the complexity of modeling the behaviour of ionic species in the two-phase system. A simple correction for partitioning of free ions was found to notably improve logD prediction accuracy in case of the most hydrophilic organic phase (butanol/water). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Computer-Aided Molecular Design Springer Journals

Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Chemistry; Physical Chemistry; Computer Applications in Chemistry; Animal Anatomy / Morphology / Histology
ISSN
0920-654X
eISSN
1573-4951
DOI
10.1007/s10822-018-0125-y
Publisher site
See Article on Publisher Site

Abstract

Performance of COSMO-RS method as a tool for partition and distribution modeling in 20 solvent pairs—composed of neutral or acidic aqueous solution and organic solvents of different polarity, ranging from alcohols to toluene and hexane—was evaluated. Experimental partition/distribution data of lignin-related and drug-like compounds (neutral, acidic, moderately basic) were used as reference. Several aspects of partition modeling were addressed: accounting for mutual saturation of aqueous and organic phases, variability of systematic prediction errors across solvent pairs, taking solute ionization into account. COSMO-RS was found to predict extraction outcome for both ligneous and drug-like compounds in various solvent pairs fairly well without any additional empirical input. The solvent-specific systematic errors were found to be moderate, despite being statistically significant, and related to the solvent hydrophobicity. Accounting for mutual solubilities of the two liquids was proven crucial in cases where water was considerably soluble in the organic solvent. The root mean square error of a priori logP prediction varied, depending mainly on the solvent pair, from 0.2 to 0.7, overall value being 0.6 log units. The accuracy was higher in case of hydrophilic than hydrophobic solvents. The logD predictions were less accurate, due to pK a prediction being an additional source of error, and also because of the complexity of modeling the behaviour of ionic species in the two-phase system. A simple correction for partitioning of free ions was found to notably improve logD prediction accuracy in case of the most hydrophilic organic phase (butanol/water).

Journal

Journal of Computer-Aided Molecular Design – Springer Journals

Published: May 30, 2018

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