Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters
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 granulationSource:
Journal of Pharmaceutical Innovation, 2019Publisher:
- Springer New York LLC
Funding / projects:
DOI: 10.1007/s12247-019-09398-0
ISSN: 1872-5120
WoS: 000591430400004
Scopus: 2-s2.0-85068957595
Collections
Institution/Community
PharmacyTY - 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 . .