The aim of the present study was to compare two DSC-based methods to predict drug-polymer solubility (melting point depression method and recrystallization method) and propose a guideline for selecting the most suitable method based on physicochemical properties of both the drug and the polymer. Using the two methods, the solubilities of celecoxib, indomethacin, carbamazepine, and ritonavir in polyvinylpyrrolidone, hydroxypropyl methylcellulose, and Soluplus® were determined at elevated temperatures and extrapolated to room temperature using the Flory-Huggins model. For the melting point depression method, it was observed that a well-defined drug melting point was required in order to predict drug-polymer solubility, since the method is based on the depression of the melting point as a function of polymer content. In contrast to previous findings, it was possible to measure melting point depression up to 20 °C below the glass transition temperature (Tg) of the polymer for some systems. Nevertheless, in general it was possible to obtain solubility measurements at lower temperatures using polymers with a low Tg. Finally, for the recrystallization method it was found that the experimental composition dependence of the Tg must be differentiable for compositions ranging from 50 to 90% drug (w/w) so that one Tg corresponds to only one composition. Based on these findings, a guideline for selecting the most suitable thermal method to predict drug-polymer solubility based on the physicochemical properties of the drug and polymer is suggested in the form of a decision tree.
Keywords: Amorphous solid dispersion; Decision tree; Differential scanning calorimetry (DSC); Flory-Huggins; Gordon-Taylor; Polymer; Solubility.
Copyright © 2018 Elsevier B.V. All rights reserved.