Towards rational design of API-poly(D, L-lactide-co-glycolide) based micro- and nanoparticles: The role of API-polymer compatibility prediction

Due to their unique properties, such as controlled drug release and improved bioavailability, polymeric microparticles and nanoparticles (MPs and NPs) have gained considerable interest in the pharmaceutical industry. Nevertheless, the high costs associated with biodegradable polymers and the active pharmaceutical ingredients (APIs) used for treating serious diseases, coupled with the vast number of API-polymer combinations, make the search for effective API-polymer MPs and NPs a costly and time-consuming process. In this work, the correlation between the compatibility of selected model APIs (i.e., ibuprofen, naproxen, paracetamol, and indomethacin) with poly(lactide-co-glycolide) (PLGA) derived from respective binary phase diagrams and characteristics of prepared MPs and NPs, such as the drug loading and solid-state properties, was investigated to probe the possibility of implementing the modeling of API-polymer thermodynamic and kinetic phase behavior as part of rational design of drug delivery systems based on MPs and NPs. API-PLGA-based MPs and NPs were formulated using an emulsion-solvent evaporation technique and were characterized for morphology, mean size, zeta potential, drug loading, and encapsulation efficiency. The solid-state properties of the encapsulated APIs were assessed using differential scanning calorimetry and X-ray powder diffraction. The evaluated compatibility was poor for all considered API-PLGA pairs, which is in alignment with the experimental results showing low drug loading in terms of amorphous API content. At the same time, drug loading of the studied APIs in terms of amorphous content was found to follow the same trend as their solubility in PLGA, indicating a clear correlation between API solubility in PLGA and achievable drug loading. These findings suggest that API-polymer phase behavior modeling and compatibility screening can be employed as an effective preformulation tool to estimate optimum initial API concentration for MP and NP preparation or, from a broader perspective, to tune or select polymeric carriers offering desired drug loading.