Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters
- PDF / 731,136 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 20 Downloads / 203 Views
ORIGINAL ARTICLE
Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters Nada Millen 1
&
Aleksandar Kovačević 2 & Jelena Djuriš 1 & Svetlana Ibrić 1
# Springer Science+Business Media, LLC, part of Springer Nature 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. Keywords Particle size distribution . Machine learning modeling . Wet granulation . Scale-up
Introduction Wet granulation is a complex technical process where powder particles, by mixing with a liquid, are turned into agglomerates and form granules. There are many factors that influence the wet granulation process such as the physical properties of the raw material (particle shape, particle size distribution (PSD), Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12247-019-09398-0) contains supplementary material, which is available to authorized users. * Nada Millen [email protected] 1
Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, Beograd 11221, Serbia
2
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia; Trg Dositeja Obradovića 6, Novi Sad 106314, Serbia
solubility, hygroscopic nature etc.), kinetics and the mechanics of the mixing process (mixer design, impeller speed), and the amount and rate of liquid addition [1]. The process of producing
Data Loading...