(654g) Successful Shelf-Life Predictions of Amorphous Solid Dispersions
AIChE Annual Meeting
2024
2024 AIChE Annual Meeting
Pharmaceutical Discovery, Development and Manufacturing Forum
Advancements in Drug Product Particle Engineering and Material Science
Thursday, October 31, 2024 - 10:06am to 10:27am
Amorphous solid dispersions (ASDs) are a state-of-the-art enabling formulation technique for poorly soluble small-molecule drugs. The bioavailability is significantly enhanced by the drug dissolution in a polymer carrier. The drugsâ recrystallization in the polymer is highly unwanted as it marks the end of shelf life of such a formulation. The current work presents a novel fast-track prediction method that combines thermodynamic factors (water absorption, fundamental crystallization driving forces), kinetic factors (glass transition, diffusivity in the ASD), drug-specific crystallization properties (nucleation, crystal growth, glass-forming ability), thermal history (spray drying, melt extrusion, aging) and their mutual impact to predict the shelf life of an ASD at any drug load/ temperature/ humidity condition. This method is developed to predict ab-initio the crystallization onset time and thus the shelf life of metastable ASDs prior to storage tests and hereby dramatically reduces the risk of late-stage product failures due to stability issues. The shelf life predictions were validated by three-years-enduring stability tests of felodipine -containing ASDs. This approach serves as robust tool for predicting the shelf life of metastable ASDs in the entire temperature/drug load/-humidity space and allows a revolutionary risk assessment of storage test results at early stage of formulation development and even before conducting the tests. It was shown that stability data obtained at so-called âacceleratedâ, harsh storage conditions within short time do not at all reflect the shelf life at more moderate storage conditions, as thermodynamic and kinetic aspects differ completely. The developed model is highly flexible with respect to potential data input and provides a revolutionary approach for ASD shelf-life optimization in the future.