(334ac) Bulk Property Prediction of Pharmaceutical Powders Via Bond Number Estimation | AIChE

(334ac) Bulk Property Prediction of Pharmaceutical Powders Via Bond Number Estimation


Kunnath, K. - Presenter, New Jersey Institute of Technology
Research Interests

This work deals with building predictive bulk property models, intended to facilitate the use of dry powder formulations in applications such as direct compaction and continuous manufacturing, for which adequate bulk properties such as packing and flow are required before commencement of production. Such property prediction models can shorten the formulation development stage, since formulators will have an approximation of the adequacy of bulk properties before experimental testing, or level of bulk property enhancement required. These models have additional significance to continuous manufacturing, where intermediate, ad-hoc property enhancement is not possible, and adequate bulk properties are required at the start of the continuous manufacturing line. In cases where bulk property enhancement is required, dry coating via nano-silica is a proven alternative to traditional dry or wet granulation, since dry coating is a relatively quick and simple step which can save time, money and resources. During dry coating, the surfaces of host pharmaceutical powders are covered with guest nano-silica particles, decreasing the inter-particle cohesion forces between host particles, and improving bulk properties. Hence, this work aims to build predictive models, which intake particle scale characteristics to predict bulk powder properties, as this will reflect changes at the particle level, such as dry coating based processing. Bond number, which is the ratio of inter-particle cohesion force to particle weight, has shown promising bulk property prediction results in recent studies. The current work will attempt to investigate the ability of Bond number models to predict bulk properties with larger data sets comprising of commonly used pharmaceutical powders, in addition to their mixtures. Results conclude that the current version of Bond number models are able to accurately predict powder bed porosity and powder flow for many commonly used pharmaceutical powders, in agreement with previous studies. However, there are several cases where Bond number model predictions are inaccurate, such as powder bed porosity of rough surfaced particles and binary mixtures where only one component is dry coated. Attempts are made to adjust the Bond number models using particle scale characteristics, such as particle shape descriptors, in order to improve prediction where it is necessary, while maintaining adequate prediction for other powders. Finally, experimental work done for model testing produced interesting trends which may be useful for pharmaceutical formulators, since they hold true for large data sets comprising of commonly used pharmaceutical powders.


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