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Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters

Authorized Users Only
2019
Authors
Millen, Nada
Kovačević, A
Đuriš, Jelena
Ibrić, Svetlana
Article (Published version)
Metadata
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Abstract
Purpose: Optimal particle size distribution (PSD) is an important factor in wet granulation in order to achieve appropriate powder flow, compactibility, and content uniformity. Parameters like D50 and surface area (SA) are used to define PSD but both are only able to compare separate fractions of a granulate. In this work, we made an attempt to characterize PSD of a final dry granulate blend and suggest novel parameters (determination coefficient R2 and trend line slope of a PSD model) to quantitatively describe PSD. Method: The significance of these parameters was tested using machine learning. Laboratory-scale samples were used for training and commercial-scale samples for testing a model. Several machine learning techniques were used to further examine the importance of these input variables using a large data set from wet granulation scale-up study. Results: The Gradient Boosted Regression Trees (GBRT) algorithm had the lowest root mean square error (RMSE) values for the several re...sponses studied (tablet tensile strength, tablet diameter and thickness, compaction work, decompaction work, and net work). The GBRT model for tablet tensile strength had an R2 model value of 0.87 and was not overfitted. The importance of input variables R2 and a was proven by the stepwise regression model’s p value (0.0003) and GBRT importance score (0.37 and 0.44, respectively). The GBRT model was the most successful in predicting decompaction work (R2 model = 0.97) with the least regularization effect. Conclusion: The proposed parameters can be used in PSD characterization and applied in critical quality attributes (CQA) prediction and wet granulation scale-up.

Keywords:
Machine learning modeling / Particle size distribution / Scale-up / Wet granulation
Source:
Journal of Pharmaceutical Innovation, 2019
Publisher:
  • Springer New York LLC
Funding / projects:
  • Infrastructure for Technology Enhanced Learning in Serbia (RS-47003)

DOI: 10.1007/s12247-019-09398-0

ISSN: 1872-5120

WoS: 000591430400004

Scopus: 2-s2.0-85068957595
[ Google Scholar ]
5
5
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3292
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Millen, Nada
AU  - Kovačević, A
AU  - Đuriš, Jelena
AU  - Ibrić, Svetlana
PY  - 2019
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3292
AB  - Purpose: Optimal particle size distribution (PSD) is an important factor in wet granulation in order to achieve appropriate powder flow, compactibility, and content uniformity. Parameters like D50 and surface area (SA) are used to define PSD but both are only able to compare separate fractions of a granulate. In this work, we made an attempt to characterize PSD of a final dry granulate blend and suggest novel parameters (determination coefficient R2 and trend line slope of a PSD model) to quantitatively describe PSD. Method: The significance of these parameters was tested using machine learning. Laboratory-scale samples were used for training and commercial-scale samples for testing a model. Several machine learning techniques were used to further examine the importance of these input variables using a large data set from wet granulation scale-up study. Results: The Gradient Boosted Regression Trees (GBRT) algorithm had the lowest root mean square error (RMSE) values for the several responses studied (tablet tensile strength, tablet diameter and thickness, compaction work, decompaction work, and net work). The GBRT model for tablet tensile strength had an R2 model value of 0.87 and was not overfitted. The importance of input variables R2 and a was proven by the stepwise regression model’s p value (0.0003) and GBRT importance score (0.37 and 0.44, respectively). The GBRT model was the most successful in predicting decompaction work (R2 model = 0.97) with the least regularization effect. Conclusion: The proposed parameters can be used in PSD characterization and applied in critical quality attributes (CQA) prediction and wet granulation scale-up.
PB  - Springer New York LLC
T2  - Journal of Pharmaceutical Innovation
T1  - Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters
DO  - 10.1007/s12247-019-09398-0
ER  - 
@article{
author = "Millen, Nada and Kovačević, A and Đuriš, Jelena and Ibrić, Svetlana",
year = "2019",
abstract = "Purpose: Optimal particle size distribution (PSD) is an important factor in wet granulation in order to achieve appropriate powder flow, compactibility, and content uniformity. Parameters like D50 and surface area (SA) are used to define PSD but both are only able to compare separate fractions of a granulate. In this work, we made an attempt to characterize PSD of a final dry granulate blend and suggest novel parameters (determination coefficient R2 and trend line slope of a PSD model) to quantitatively describe PSD. Method: The significance of these parameters was tested using machine learning. Laboratory-scale samples were used for training and commercial-scale samples for testing a model. Several machine learning techniques were used to further examine the importance of these input variables using a large data set from wet granulation scale-up study. Results: The Gradient Boosted Regression Trees (GBRT) algorithm had the lowest root mean square error (RMSE) values for the several responses studied (tablet tensile strength, tablet diameter and thickness, compaction work, decompaction work, and net work). The GBRT model for tablet tensile strength had an R2 model value of 0.87 and was not overfitted. The importance of input variables R2 and a was proven by the stepwise regression model’s p value (0.0003) and GBRT importance score (0.37 and 0.44, respectively). The GBRT model was the most successful in predicting decompaction work (R2 model = 0.97) with the least regularization effect. Conclusion: The proposed parameters can be used in PSD characterization and applied in critical quality attributes (CQA) prediction and wet granulation scale-up.",
publisher = "Springer New York LLC",
journal = "Journal of Pharmaceutical Innovation",
title = "Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters",
doi = "10.1007/s12247-019-09398-0"
}
Millen, N., Kovačević, A., Đuriš, J.,& Ibrić, S.. (2019). Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters. in Journal of Pharmaceutical Innovation
Springer New York LLC..
https://doi.org/10.1007/s12247-019-09398-0
Millen N, Kovačević A, Đuriš J, Ibrić S. Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters. in Journal of Pharmaceutical Innovation. 2019;.
doi:10.1007/s12247-019-09398-0 .
Millen, Nada, Kovačević, A, Đuriš, Jelena, Ibrić, Svetlana, "Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters" in Journal of Pharmaceutical Innovation (2019),
https://doi.org/10.1007/s12247-019-09398-0 . .

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