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

活性成分 混合(物理) 可制造性设计 医药制造业 工艺工程 压缩(物理) 材料科学 计算机科学 生物系统 机械工程 工程类 复合材料 物理 生物信息学 量子力学 生物
作者
Magdalini Aroniada,Gabriele Bano,Yuliya Vueva,Charalampos Christodoulou,Feng Li,James D. Litster
出处
期刊:International Journal of Pharmaceutics [Elsevier]
卷期号:647: 123475-123475 被引量:1
标识
DOI:10.1016/j.ijpharm.2023.123475
摘要

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.

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