A novel mixing rule model to predict the flowability of directly compressed pharmaceutical blends

• Published in: International journal of pharmaceutics 2023-11, Vol.647, p.123475-123475, Article 123475
• Main Authors: Aroniada, Magdalini, Bano, Gabriele, Vueva, Yuliya, Christodoulou, Charalampos, Li, Feng, Litster, James D.
• Format: Article
• Language: English
• ISSN: 0378-5173; 1873-3476
• DOI: 10.1016/j.ijpharm.2023.123475

In the pharmaceutical industry, powder flowability is an essential manufacturability attribute to consider when selecting the suitable manufacturing route and formulation. The selection of the formulation is usually based on the physical and chemical properties of the Active Pharmaceutical Ingredient (API) under consideration. Current industrial practice heavily relies on experimental work, which often results in significant labor and API consumption that results in higher costs. In this study we describe the development of a mixing rule to predict powder blend flowability from the flow properties of the individual components for industrial formulations manufactured via Direct Compression (DC). The mixing rule assumes that the granular solids' interactions are dominated by cohesive forces but are pragmatic to calibrate from the perspective of the typical data collated in an industrial environment. The proposed model was validated using a range of different APIs and the results show that the model can effectively predict the flowability properties of any formulation across the space of DC-relevant formulation compositions. Finally, a connection between the model and APIs properties (shape and size) was investigated via a linear correlation between the API particle properties and interparticle forces.