Barjat, H, Checkley, S, Chitu, T et al. (11 more authors) (2021) Demonstration of the Feasibility of Predicting the Flow of Pharmaceutically Relevant Powders from Particle and Bulk Physical Properties. Journal of Pharmaceutical Innovation, 16 (1). pp. 181-196. ISSN 1872-5120
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, Springer Verlag. This is an author produced version of a paper published in Journal of Pharmaceutical Innovation Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Mar 2020 14:22 |
Last Modified: | 27 Jun 2022 10:47 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/s12247-020-09433-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158693 |