Abstract:In this work, the solubilities of pharmaceutical cocrystals in solvent/anti-solvent systems were predicted using PC-SAFT in order to increase the efficiency of cocrystal formation processes. Modeling results and experimental data were compared for the cocrystal system nicotinamide/succinic acid (2:1) in the solvent/anti-solvent mixtures ethanol/water, ethanol/acetonitrile and ethanol/ethyl acetate at 298.15 K and in the ethanol/ethyl acetate mixture also at 310.15 K. The solubility of the investigated cocrystal slightly increased when adding small amounts of anti-solvent to the solvent, but drastically decreased for high anti-solvent amounts. Furthermore, the solubilities of nicotinamide, succinic acid and the cocrystal in the considered solvent/anti-solvent mixtures showed strong deviations from ideal-solution behavior. However, by accounting for the thermodynamic non-ideality of the components, PC-SAFT is able to predict the solubilities in all above-mentioned solvent/anti-solvent systems in good agreement with the experimental data.
A setup for online measurement of crystallization processes was developed, where particle images were taken and in combination with artificial neural networks (ANNs) were used to convert the particle size distribution into populations of single crystals and agglomerates. For generation of an ANN that discriminates all particle sizes equally well, the training set composition was investigated while proportional similarity was applied for image descriptor selection. Adipic acid/water served as a model system. The effect of the measurement setup on experiments performed for normal cooling crystallization was evaluated. It was found that the major challenge of measuring crystal agglomerates lies in superimposing aggregates that falsify the online measurement results, as offline measurements demonstrated.
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