(588a) Using Polymers As Solvents for Active Pharmaceutical Ingredients

Active pharmaceutical ingredients (APIs) are often complex compounds exhibiting a very low aqueous solubility. Therefore, they only slowly dissolve in the body when administered as crystalline solids, which leads to a very low bioavailability. For this reason, about 80% of the promising APIs currently under development will never make it into a medicine. In contrast, amorphous API forms exhibit higher solubility as well as a higher dissolution rates than the crystalline API forms. However, they a thermodynamically less stable and will recrystallize upon storage or in solution.

One possibility to stabilize amorphous APIs is to molecularly dissolve them in an amorphous polymer which acts as solvent and carrier matrix. However, the API loading in pharmaceutical formulations usually exceeds the API solubility in the polymer. Therefore, these formulations are usually not thermodynamically stable, but amorphous phase separation and even recrystallization of the API might occur during storage. This depends on the thermodynamic phase behaviour and is to a great extend influenced by the kind of API and polymer, by temperature, and by relative humidity.

Due to this complexity, these API/polymer formulations are so far usually found by trial-and-error procedures. Thermodynamic understanding and modeling of the underlying phenomena, however, is a valuable tool to improve and to intensify this process.                                                                                                                                     

The talk will give an overview about the thermodynamic phase behaviour of API/polymer systems and in particular about the influence of temperature and relative humidity. The phenomena will be discussed on the basis of experimental data for various systems. Finally, it will be shown that thermodynamic modelling today allows for reliable correlations and even predictions of the phase behaviour of these formulations. It thus can drastically reduce the experimental effort for developing the optimal API formulation and processing.