TY - JOUR
T1 - Predicting pharmaceutical powder flow from microscopy images using deep learning
AU - Wilkinson, Matthew
AU - Pereira Diaz, Laura
AU - Vassileiou, Antony D
AU - Armstrong, John
AU - Brown, Cameron
AU - Castro Dominguez, Bernardo
AU - Florence, Alistair
N1 - The authors thank EPSRC and the EPSRC Future Continuous Manufacturing and Advanced Crystallization Research Hub (Grant Ref. EP/P006965/1) for funding this work. The authors acknowledge that parts of this work were carried out in the CMAC National Facility supported by a UK Research Partnership Fund (UKRPIF) award from the Higher Education Funding Council for England (HEFCE) (Grant Ref. HH13054). M. R. W thanks the PhD studentship funded by CMAC Future Manufacturing Research Hub and the Centre for Sustainable and Circular Technologies at the University of Bath. All the authors thank Dr Tom Fincham Haines and the Department of Computer Science at the University of Bath for their support in accessing the hardware resources needed for this work.
Data availability
The source code and experimental data for this project is available at https://github.com/MRW-Code/cmac_particle_flow.
PY - 2023/2/13
Y1 - 2023/2/13
N2 - The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show cohesive behaviour. For materials that are cohesive, processing methods before tabletting, such as granulation, are required. Flowability measurements require large quantities of materials, significant time and human investments and repeat testing due to a lack of reproducible results when taking experimental measurements. This process is particularly challenging during the early-stage development of a new formulation when the amount of material is limited. To overcome these challenges, we present the use of deep learning methods to predict powder flow from images of pharmaceutical materials. We achieve 98.9% validation accuracy using images which by eye are impossible to extract meaningful particle or flowability information from. Using this approach, the need for experimental powder flow characterization is reduced as our models rely on images which are routinely captured as part of the powder size and shape characterization process. Using the imaging method recorded in this work, images can be captured with only 500 mg of material in just 1 hour. This completely removes the additional 30 g of material and extra measurement time needed to carry out repeat testing for traditional flowability measurements. This data-driven approach can be better applied to early-stage drug development which is by nature a highly iterative process. By reducing the material demand and measurement times, new pharmaceutical products can be developed faster with less material, reducing the costs, limiting material waste and hence resulting in a more efficient, sustainable manufacturing process. This work aims to improve decision-making for manufacturing route selection, achieving the key goal for digital design of being able to better predict properties while minimizing the amount of material required and time to inform process selection during early-stage development.
AB - The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show cohesive behaviour. For materials that are cohesive, processing methods before tabletting, such as granulation, are required. Flowability measurements require large quantities of materials, significant time and human investments and repeat testing due to a lack of reproducible results when taking experimental measurements. This process is particularly challenging during the early-stage development of a new formulation when the amount of material is limited. To overcome these challenges, we present the use of deep learning methods to predict powder flow from images of pharmaceutical materials. We achieve 98.9% validation accuracy using images which by eye are impossible to extract meaningful particle or flowability information from. Using this approach, the need for experimental powder flow characterization is reduced as our models rely on images which are routinely captured as part of the powder size and shape characterization process. Using the imaging method recorded in this work, images can be captured with only 500 mg of material in just 1 hour. This completely removes the additional 30 g of material and extra measurement time needed to carry out repeat testing for traditional flowability measurements. This data-driven approach can be better applied to early-stage drug development which is by nature a highly iterative process. By reducing the material demand and measurement times, new pharmaceutical products can be developed faster with less material, reducing the costs, limiting material waste and hence resulting in a more efficient, sustainable manufacturing process. This work aims to improve decision-making for manufacturing route selection, achieving the key goal for digital design of being able to better predict properties while minimizing the amount of material required and time to inform process selection during early-stage development.
U2 - 10.1039/D2DD00123C
DO - 10.1039/D2DD00123C
M3 - Article
SN - 2635-098X
JO - Digital Discovery
JF - Digital Discovery
ER -
