Stages in spheronisation: Evolution of pellet size and shape during spheronisation of microcrystalline cellulose-based paste extrudates

Elucidation of mass transfer mechanisms in pellet formation by spheronization

Previously published mechanisms of pellet formation during extrusion-spheronization include a transfer of material between different granules. This research aimed to specify the origin of this transfered mass, enabling further insight into the extrusion-spheronization process. Granules of various diameters were rounded simultaniously in a spheronizer to ascertain if mass transfer between smaller and larger granules is truly in balance, or if mass transfer from smaller to larger granules is preferred. Granules were also marked with a fluorescent tracer to enable quantification of mass transfer. By using differently sized and shaped granules as starting material, different modes of mass transfer were investigated. Samples were taken after various process durations to investigate the kinetics of the tranfer mechanism. It was found that both small and large granules dispense and receive mass during spheronization. In general, small granules increase their size, while large granules maintain their size or show a slight size decrease, resulting in the particularly narrow monomodal size distribution.

A micro-XRT image analysis and machine learning methodology for the characterisation of multi-particulate capsule formulations

The application of X-ray microtomography for quantitative structural analysis of pharmaceutical multi-particulate systems was demonstrated for commercial capsules, each containing approximately 300 formulated ibuprofen pellets. The implementation of a marker-supported watershed transformation enabled the reliable segmentation of the pellet population for the 3D analysis of individual pellets. Isolated translation- and rotation-invariant object cross-sections expanded the applicability to additional 2D image analysis techniques. The full structural characterisation gave access to over 200 features quantifying aspects of the pellets' size, shape, porosity, surface and orientation. The extracted features were assessed using a ReliefF feature selection method and a supervised Support Vector Machine learning algorithm to build a model for the detection of broken pellets within each capsule. Data of three features from distinct structure-related categories were used to build classification models with an accuracy of more than 99.55% and a minimum precision of 86.20% validated with a test dataset of 886 pellets. This approach to extract quantitative information on particle quality attributes combined with advanced data analysis strategies has clear potential to directly inform manufacturing processes, accelerating development and optimisation.

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Coupling micro-XRT analysis with feature selection and machine learning for advanced pharmaceutical product characterisation.

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Information on particle 3D-orientation were utilised to extract translation- and rotation-invariant object cross-sections.

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Successful extraction of over 200 quantitative pellet descriptors linked to size, shape, porosity, surface and orientation.

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Sensitivity analysis and ReliefF feature selection approach to identify predictive features for pellet classification.

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Feature-based binary SVM classification model for the detection of broken pellets within the formulated system.

WITHDRAWN: A micro-XRT Image Analysis and Machine Learning Methodology for the Characterisation of Multi-Particulate Capsule Formulations

The Publisher regrets that this article is an accidental duplication of a published article,https://doi.org/10.1016/j.ijpx.2020.100041. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.