(492a) Packing Prediction of Pharmaceutical Powders Via Bond Number Estimation

The packing of pharmaceutical powders plays an important role in various unit operations such as die filling, compaction, and feeder performance. In the case of pharmaceutical tablets, the final tablet porosity can dictate critical tablet properties such as tensile strength and dissolution kinetics, and is dependent on blend porosity. Therefore, pharmaceutical powder or blend porosity is an important property that requires better understanding of how it is impacted by both mixing process parameters and constituent material properties. Recent work has introduced mechanistic models to predict the porosity of pharmaceutical powders, which employs a semi-empirical approximation using commonly measured particulate properties such as particle size, particle density, and surface energy. Here, this initial work has been extended to include a wider range of active pharmaceutical ingredients (APIs) and high functional pharmaceutical excipients, both before and after dry coating with nano-silica. Results show that the recently introduced porosity prediction model can predict most pharmaceutical powder porosities fairly accurately, but such is not the case for certain types of materials including functional excipients such as Avicel grade microcrystalline cellulose (MCC) excipients. These functional excipients have a distinctly rougher particle surface, compared to smooth particle surfaces for most other pharmaceutical powders. This distinction could be the key to improving the proposed porosity prediction model, and such a topic has been discussed in previous literature. In this work, we also examine how a prevalent approximation of natural surface asperity size of 200 nm diameter requires re-examination. The specific surface area, internal void volume and particle shape descriptors of the pharmaceutical powders were experimentally measured, and the dependence of powder packing on these characteristics was investigated. Additionally, binary blends of APIs and commonly used excipients were tested for their bulk density (packing). Results show that API dry coating significantly alters the interparticle cohesiveness of these binary blends, and their packing behavior is more similar to non-cohesive glass beads. Such processing significantly improves the design space for micronized, cohesive APIs for use in processes such as high speed direct compression. However, extensions of the current models are also necessary for accurate blend porosity prediction if dry coating processing was used for one or more blend constituents. The experimental work presented here illustrates the need to improve the current semi-empirical, mechanistic porosity prediction model and may help assess if our initial attempts are adequate.