TY  - JOUR
AU  - Millen, Nada
AU  - Kovacević, Aleksandar
AU  - Khera, Lalit
AU  - Đuriš, Jelena
AU  - Ibrić, Svetlana
PY  - 2019
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3245
AB  - The purpose of this extensive study is to use a quality by design (QbD) approach and multiple machine learning algorithms in facilitating wet granulation process scale-up. This study investigated the extent of influence of both formulation and process variables. Furthermore, measured responses covered compressibility, compactibility and manufacturability of a powder blend. Finally, the models developed on laboratory scale samples were tested on pilot and commercial scale runs. Tablet detachment and ejection work were calculated from force-displacement measurements. Significant numerical and categorical input variables were identified by using a stepwise regression model and their importance evaluated by using a boosted trees model. Pilot scale runs resulted in the highest tablet tensile strength and compaction work as well as the highest detachment and ejection work. Critical quality attributes (CQAs) that were the most successfully predicted were the compaction, decompaction, and net work, as well as the tablet height. The most important input variable influencing all CQAs was the compaction force. Application of the boosted regression trees model resulted in the lowest Root Mean Square Error (RMSE) values for all of the responses. This work demonstrates reliability of predictions of developed models that can be successfully used as a part of a QbD approach for wet granulation scale-up.
PB  - Savez hemijskih inženjera, Beograd
T2  - Hemijska industrija
T1  - Machine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parameters
VL  - 73
IS  - 3
SP  - 155
EP  - 168
DO  - 10.2298/HEMIND190412017M
ER  - 

@article{
author = "Millen, Nada and Kovacević, Aleksandar and Khera, Lalit and Đuriš, Jelena and Ibrić, Svetlana",
year = "2019",
abstract = "The purpose of this extensive study is to use a quality by design (QbD) approach and multiple machine learning algorithms in facilitating wet granulation process scale-up. This study investigated the extent of influence of both formulation and process variables. Furthermore, measured responses covered compressibility, compactibility and manufacturability of a powder blend. Finally, the models developed on laboratory scale samples were tested on pilot and commercial scale runs. Tablet detachment and ejection work were calculated from force-displacement measurements. Significant numerical and categorical input variables were identified by using a stepwise regression model and their importance evaluated by using a boosted trees model. Pilot scale runs resulted in the highest tablet tensile strength and compaction work as well as the highest detachment and ejection work. Critical quality attributes (CQAs) that were the most successfully predicted were the compaction, decompaction, and net work, as well as the tablet height. The most important input variable influencing all CQAs was the compaction force. Application of the boosted regression trees model resulted in the lowest Root Mean Square Error (RMSE) values for all of the responses. This work demonstrates reliability of predictions of developed models that can be successfully used as a part of a QbD approach for wet granulation scale-up.",
publisher = "Savez hemijskih inženjera, Beograd",
journal = "Hemijska industrija",
title = "Machine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parameters",
volume = "73",
number = "3",
pages = "155-168",
doi = "10.2298/HEMIND190412017M"
}

Millen, N., Kovacević, A., Khera, L., Đuriš, J.,& Ibrić, S.. (2019). Machine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parameters. in Hemijska industrija
Savez hemijskih inženjera, Beograd., 73(3), 155-168.
https://doi.org/10.2298/HEMIND190412017M

Millen N, Kovacević A, Khera L, Đuriš J, Ibrić S. Machine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parameters. in Hemijska industrija. 2019;73(3):155-168.
doi:10.2298/HEMIND190412017M .

Millen, Nada, Kovacević, Aleksandar, Khera, Lalit, Đuriš, Jelena, Ibrić, Svetlana, "Machine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parameters" in Hemijska industrija, 73, no. 3 (2019):155-168,
https://doi.org/10.2298/HEMIND190412017M . .