A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends
This paper presents a system of hybrid models that combine both mechanistic and data-driven approaches to predict physical powder blend properties from their raw component properties. Mechanistic, probabilistic models were developed to predict the particle size and shape, represented by aspect ratio...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | International Journal of Pharmaceutics: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590156724000707 |
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| author | Mohammad Salehian Jonathan Moores Jonathan Goldie Isra' Ibrahim Carlota Mendez Torrecillas Ishwari Wale Faisal Abbas Natalie Maclean John Robertson Alastair Florence Daniel Markl |
| author_facet | Mohammad Salehian Jonathan Moores Jonathan Goldie Isra' Ibrahim Carlota Mendez Torrecillas Ishwari Wale Faisal Abbas Natalie Maclean John Robertson Alastair Florence Daniel Markl |
| author_sort | Mohammad Salehian |
| collection | DOAJ |
| description | This paper presents a system of hybrid models that combine both mechanistic and data-driven approaches to predict physical powder blend properties from their raw component properties. Mechanistic, probabilistic models were developed to predict the particle size and shape, represented by aspect ratio, distributions of pharmaceutical blends using those of the raw components. Additionally, the accuracy of existing mixture rules for predicting the blend's true density and bulk density was assessed. Two data-driven models were developed to estimate the mixture's tapped density and flowability (represented by the flow function coefficient, FFC) using data from 86 mixtures, which utilized the principal components of predicted particle size and shape distributions in combination with the true density, and bulk density as input data, saving time and material by removing the need for resource-intensive shear testing for raw components. A model-based uncertainty quantification technique was designed to analyse the precision of model-predicted FFCs. The proposed particle size and shape mixture models outperformed the existing approach (weighted average of distribution percentiles) in terms of prediction accuracy while providing insights into the full distribution of the mixture. The presented hybrid system of models accurately predicts the mixture properties of different formulations and components with often R2>0.8, utilising raw material properties to reduce time and material resources on preparing and characterising blends. |
| format | Article |
| id | doaj-art-674e035279f54ed38ca1c02d8c683805 |
| institution | Kabale University |
| issn | 2590-1567 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Pharmaceutics: X |
| spelling | doaj-art-674e035279f54ed38ca1c02d8c6838052024-12-17T05:00:41ZengElsevierInternational Journal of Pharmaceutics: X2590-15672024-12-018100298A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blendsMohammad Salehian0Jonathan Moores1Jonathan Goldie2Isra' Ibrahim3Carlota Mendez Torrecillas4Ishwari Wale5Faisal Abbas6Natalie Maclean7John Robertson8Alastair Florence9Daniel Markl10Digital Medicines Manufacturing (DM2) Research Centre, Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK; Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKCentre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKDigital Medicines Manufacturing (DM2) Research Centre, Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK; Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKCentre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKCentre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKCentre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKDigital Medicines Manufacturing (DM2) Research Centre, Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK; Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKCentre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKCentre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKDigital Medicines Manufacturing (DM2) Research Centre, Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK; Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UKDigital Medicines Manufacturing (DM2) Research Centre, Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK; Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK; Corresponding author at: Digital Medicines Manufacturing (DM2) Research Centre, Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC), Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow, UK.This paper presents a system of hybrid models that combine both mechanistic and data-driven approaches to predict physical powder blend properties from their raw component properties. Mechanistic, probabilistic models were developed to predict the particle size and shape, represented by aspect ratio, distributions of pharmaceutical blends using those of the raw components. Additionally, the accuracy of existing mixture rules for predicting the blend's true density and bulk density was assessed. Two data-driven models were developed to estimate the mixture's tapped density and flowability (represented by the flow function coefficient, FFC) using data from 86 mixtures, which utilized the principal components of predicted particle size and shape distributions in combination with the true density, and bulk density as input data, saving time and material by removing the need for resource-intensive shear testing for raw components. A model-based uncertainty quantification technique was designed to analyse the precision of model-predicted FFCs. The proposed particle size and shape mixture models outperformed the existing approach (weighted average of distribution percentiles) in terms of prediction accuracy while providing insights into the full distribution of the mixture. The presented hybrid system of models accurately predicts the mixture properties of different formulations and components with often R2>0.8, utilising raw material properties to reduce time and material resources on preparing and characterising blends.http://www.sciencedirect.com/science/article/pii/S2590156724000707Computational modelPharmaceutical mixturesParticle sizeParticle shapeBulk densityTapped density |
| spellingShingle | Mohammad Salehian Jonathan Moores Jonathan Goldie Isra' Ibrahim Carlota Mendez Torrecillas Ishwari Wale Faisal Abbas Natalie Maclean John Robertson Alastair Florence Daniel Markl A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends International Journal of Pharmaceutics: X Computational model Pharmaceutical mixtures Particle size Particle shape Bulk density Tapped density |
| title | A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends |
| title_full | A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends |
| title_fullStr | A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends |
| title_full_unstemmed | A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends |
| title_short | A hybrid system of mixture models for the prediction of particle size and shape, density, and flowability of pharmaceutical powder blends |
| title_sort | hybrid system of mixture models for the prediction of particle size and shape density and flowability of pharmaceutical powder blends |
| topic | Computational model Pharmaceutical mixtures Particle size Particle shape Bulk density Tapped density |
| url | http://www.sciencedirect.com/science/article/pii/S2590156724000707 |
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