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

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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.

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Acknowledgements

We are grateful for the helpful comments of Bruno Hancock, Dan Blackwood, Conrad Davies and Martyn Ticehurst (Pfizer), and useful insights were provided by Stefan Lines, James Clarke and Khezia Asamoah (Bristol-Myers Squibb).

Funding

This work was carried out as part of the ADDoPT Consortium (www.addopt.org), which received funding from Finance Birmingham (UK) as part of the AMSCI initiative.

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Authors and Affiliations

  1. Daresbury Laboratory, Sci-Tech Daresbury, STFC, Keckwick Lane, Daresbury, Warrington, WA4 4AD, UK

    Hervé Barjat, Stephen Checkley & Chris Morris

  2. GSK R&D Ware, Chemical Development – Materials Science,, Hertfordshire, SG12 0DP, UK

    Toma Chitu

  3. Pfizer Worldwide R&D, Discovery Park House, Ramsgate Road, Sandwich, UK

    Neil Dawson

  4. School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK

    Amin Farshchi

  5. Bristol-Myers Squibb, Reeds Lane, Moreton, Wirral, CH46 1QW, UK

    Ana Ferreira, John Gamble, Michael Leane & Mike Tobyn

  6. Perceptive Engineering Limited, Vanguard House, Keckwick Ln, Daresbury, WA4 4AB, UK

    Andy Mitchell & Furqan Tahir

  7. Technical Excellence Oral Solid Dosage, Global Manufacturing & Supply (GMS), GlaxoSmithKline, Brentford, UK

    Kendal Pitt

  8. AstraZeneca Pharmaceuticals, Silk Road Business Park, Macclesfield, SK10 2NA, UK

    Richard Storey

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  1. Hervé Barjat
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  2. Stephen Checkley
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Correspondence to Mike Tobyn.

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Barjat, H., Checkley, S., Chitu, T. et al. Demonstration of the Feasibility of Predicting the Flow of Pharmaceutically Relevant Powders from Particle and Bulk Physical Properties. J Pharm Innov 16, 181–196 (2021). https://doi.org/10.1007/s12247-020-09433-5

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  • DOI: https://doi.org/10.1007/s12247-020-09433-5

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