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

Doerr, Frederik J.S. and Florence, Alastair J. (2020) A micro-XRT image analysis and machine learning methodology for the characterisation of multi- particulate capsule formulations. International Journal of Pharmaceutics: X, 2. 100041. ISSN 2590-1567 (https://doi.org/10.1016/j.ijpx.2020.100041)

[thumbnail of Doerr-Florence-IJP-2020-machine-learning-methodology-for-the-characterisation-of-multi-particulate-capsule-formulations]
Text. Filename: Doerr_Florence_IJP_2020_machine_learning_methodology_for_the_characterisation_of_multi_particulate_capsule_formulations.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (2MB)| Preview


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.


Doerr, Frederik J.S. ORCID logoORCID: https://orcid.org/0000-0001-5245-0503 and Florence, Alastair J. ORCID logoORCID: https://orcid.org/0000-0002-9706-8364;