A pragmatic mixing model for the evaluation of powder flow properties of multicomponent pharmaceutical blends
Maintaining flowability of pharmaceutical blends is critical for operational efficiency in state-of-the-art continuous direct compression (CDC) manufacturing, with poor flow potentially resulting in API loss, increased experimental work and increased time to market. Consequently, flowability is a cr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | International Journal of Pharmaceutics: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590156725000246 |
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| Summary: | Maintaining flowability of pharmaceutical blends is critical for operational efficiency in state-of-the-art continuous direct compression (CDC) manufacturing, with poor flow potentially resulting in API loss, increased experimental work and increased time to market. Consequently, flowability is a crucial consideration in the design of formulations and must be considered throughout the development process when changes are introduced. Traditionally, understanding flow properties has required testing large amounts of material, particularly when evaluating formulation options. This has led to research into developing predictive flow models to reduce experimental burden. Current models with good predictive capacity, such as using granular bond number, require non-routine measurements such as mechanical surface energy. Three mixture designs, each using three pharmaceutical materials, were developed to investigate flow properties and allow the evaluation of a number of mixing models for predicting flowability with minimal experimental input requirements. The resultant models ranged in complexity from simple first order mixture models to more complex third order models with binary and ternary interaction parameters. An analysis of the experimental cost versus prediction accuracy suggested that while the more complex models delivered the most accurate predictions, a first order mass weighted model using inverse FFC was capable of providing good predictions in return for a more manageable experimental burden, with an R2 value of 0.68, root mean square error of 2.88 and a mean absolute percentage error of 0.21. This model has the potential to provide valuable insights during early formulation design and development where material is scarce and good flowability is crucial. |
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| ISSN: | 2590-1567 |