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 pKa 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).

Systematic Optimization of Liquid-Liquid Extraction for Isolation of Unidentified Components.

We present a systematic approach for predicting the best solvents for selective extraction of components with unknown structure from complex mixtures (e.g., natural products)-a tool promising dramatic simplification of extraction process optimization. Its key advantage is that identification of the component(s) is unnecessary-prediction is based on a small set of experimental distribution coefficients (obtained using a combination of shake-flask extraction and chromatographic analysis) rather than structure-based descriptors. The methodology is suitable for the very common situations in practice where the desired compound needs to be separated from unknown impurities (i.e., selectively extracted from the mixture), as well as for large-scale and high-throughput work. The proof-of-concept methodology was developed and evaluated using an extensive set of experimental distribution data of lignin-related compounds obtained in this work.