Liquid–liquid
extraction is a potential separation process
for the purification of active pharmaceutical ingredients (APIs).
The design of an extraction step requires knowledge of the API partition
coefficient, which strongly depends on the solvent system and process
conditions. Usually, cost-intensive experiments have to be performed
to select the most suitable solvent system and the best process conditions.
The number of experiments can be reduced by predicting the partition
coefficient using perturbed chain statistical associating theory (PC-SAFT).
In this work, modeling results and experimental data were compared
for the partition coefficients of the APIs nicotinamide and salicylamide
in different solvent systems at temperatures from 293.15 to 328.15
K and at pH values varying between 5.2 and 10.3. The results show
that PC-SAFT is able to predict the API partition coefficients for
different solvent systems as functions of temperature and pH.
Liquid−liquid extraction is a potential separation process for the purification and isolation of pharmaceuticals. However, as considerable experimental effort is required to choose an adequate extractant, liquid−liquid extraction is rarely used in the pharmaceutical industry. By applying a thermodynamic model to predict the extraction behavior of pharmaceuticals, the experimental effort required to select a suitable extractant can be decreased substantially. This work demonstrates that PC-SAFT is able to predict the extraction behavior of pharmaceuticals based solely on solid solubility data of the pharmaceutical in pure solvents. Because these data are required for pharmaceutical licensing and registration, they are usually available. To demonstrate the power of the modeling tool, six ternary two-phase systems containing a pharmaceutical intermediate or its impurity were modeled with PC-SAFT. The modeling results for the extraction behavior of the two pharmaceuticals were found to be in good agreement with the experimental data.
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