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Graphical abstract  An important challenge to overcome in the solid dosage forms technology is the selection of the most biopharmaceutically efficient polymeric excipients. The excipients can be selected, among others, by compatibility studies since incompatibilities between ingredients of the drug formulations adversely affect their bioavailability, stability, efficacy, and safety. Therefore, new, fast, and reliable methods for detecting incompatibility are constantly being sought. Hence, the purpose of this work was to assess the usefulness of a heating, cooling, and reheating differential scanning calorimetry (DSC) program for detecting potential incompatibilities between atenolol, an active pharmaceutical ingredient (API), and polymeric excipients. Hot-stage microscopy (HSM), Fourier transform infrared (FTIR) spectroscopy, and powder X-ray diffraction (PXRD) were used as supporting techniques. Additionally, principal component analysis (PCA) and hierarchical cluster analysis (HCA) served as tools to support the interpretation of the data acquired from the DSC curves and FTIR spectra. As the alterations in the shape of the DSC peak of atenolol which are indicative of incompatibility are visible only on the cooling and reheating curves of the mixtures, the DSC heating–cooling–reheating program was found to be very useful for identifying potential incompatibilities in the binary mixtures of atenolol and polymeric excipients. The melting and recrystallization of atenolol alone and in its mixtures were also confirmed by HSM, while FTIR displayed changes in the spectra of mixtures due to incompatibility. These studies revealed that atenolol is incompatible with hydroxyethylcellulose, hypromellose, and methylcellulose. PXRD measurements at room temperature revealed that the crystallinity of atenolol did not change in these mixtures. However, its crystallinity was reduced in the mixtures previously heated up to 155 °C and then cooled to 25 °C.

Quantification of Compatibility Between Polymeric Excipients and Atenolol Using Principal Component Analysis and Hierarchical Cluster Analysis