Demonstration of the Feasibility of Predicting the Flow of Pharmaceutically Relevant Powders from Particle and Bulk Physical Properties

Hervé Barjat, Stephen Checkley, Toma Chitu, Neil Dawson, Amin Farshchi, Ana Ferreira, John Gamble, Michael Leane, Andy Mitchell, Chris Morris, Kendal Pitt, Richard Storey, Furqan Tahir, Mike Tobyn

Research output: Contribution to journalArticle

Abstract

Purpose: Understanding and predicting the flow of bulk pharmaceutical materials could be key in enabling pharmaceutical manufacturing by continuous direct compression (CDC). This study examines whether, by taking powder and bulk measurements, and using statistical modelling, it would be possible to predict the flow of a range of materials likely to be used in CDC. Methods: More than 100 materials were selected for study, from four pharmaceutical companies. Particle properties were measured by static image analysis, powder surface area and surface energy techniques, and flow by shear cell measurements. The data was then analysed, and a range of statistical modelling techniques were used to build predictive models for flow. Results: Using the results from static image analysis, a model could be built which allowed the prediction of likely flow in a shear cell, which can be related to performance in a CDC system. Only a small amount of powder was required for the image analysis. Surface area did not add to the precision of the model, and the available surface energy technique did not correlate with flow. Conclusions: A small sample of powder can be examined by static image analysis, and this data can be used to give an early read on likely flow of a material in a CDC system or other pharmaceutical process, allowing early intervention (if necessary) to improve the characteristics of a material, early in development.
Original languageEnglish
JournalJournal of Pharmaceutical Innovation
DOIs
Publication statusPublished - 8 Feb 2020

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