Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes
- Author
- Kensaku Matsunami (UGent) , Tomohiro Miura, Keita Yaginuma, Shuichi Tanabe, Sara Badr and Hirokazu Sugiyama
- Organization
- Abstract
- This paper presents surrogate models of dissolution behavior to identify the critical input parameters in tablet manufacturing processes. Dissolution behavior is a critical quality attribute, and is defined as the time profile of fraction of dissolved active pharmaceutical ingredients in a solvent from a tablet. Dissolution behavior was first calculated by mechanistic models using gPROMS (R) and fitted by the Weibull model. Random forest regression calculated the Weibull model parameters from the input model parameters. The developed models enabled to reduce computational cost without losing accuracy. Case studies were performed for both dry and wet granu-lation processes using paracetamol. The results identified key input and intermediate parameters to the disso-lution behavior, e.g., granule bulk density and porosity. In addition, wet granulation had a tendency to make dissolution faster than dry granulation. The proposed approach can contribute to enhance simulation capabilities in the limited time of pharmaceutical process development.
- Keywords
- Computer Science Applications, General Chemical Engineering, Sensitivity analysis, Machine learning, Roller compaction, Wet granulation, Process design, Pharmaceutical manufacturing
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GTXZHEW8AQV73RJFA5NG27DK
- MLA
- Matsunami, Kensaku, et al. “Surrogate Modeling of Dissolution Behavior toward Efficient Design of Tablet Manufacturing Processes.” COMPUTERS & CHEMICAL ENGINEERING, vol. 171, 2023, doi:10.1016/j.compchemeng.2023.108141.
- APA
- Matsunami, K., Miura, T., Yaginuma, K., Tanabe, S., Badr, S., & Sugiyama, H. (2023). Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes. COMPUTERS & CHEMICAL ENGINEERING, 171. https://doi.org/10.1016/j.compchemeng.2023.108141
- Chicago author-date
- Matsunami, Kensaku, Tomohiro Miura, Keita Yaginuma, Shuichi Tanabe, Sara Badr, and Hirokazu Sugiyama. 2023. “Surrogate Modeling of Dissolution Behavior toward Efficient Design of Tablet Manufacturing Processes.” COMPUTERS & CHEMICAL ENGINEERING 171. https://doi.org/10.1016/j.compchemeng.2023.108141.
- Chicago author-date (all authors)
- Matsunami, Kensaku, Tomohiro Miura, Keita Yaginuma, Shuichi Tanabe, Sara Badr, and Hirokazu Sugiyama. 2023. “Surrogate Modeling of Dissolution Behavior toward Efficient Design of Tablet Manufacturing Processes.” COMPUTERS & CHEMICAL ENGINEERING 171. doi:10.1016/j.compchemeng.2023.108141.
- Vancouver
- 1.Matsunami K, Miura T, Yaginuma K, Tanabe S, Badr S, Sugiyama H. Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes. COMPUTERS & CHEMICAL ENGINEERING. 2023;171.
- IEEE
- [1]K. Matsunami, T. Miura, K. Yaginuma, S. Tanabe, S. Badr, and H. Sugiyama, “Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes,” COMPUTERS & CHEMICAL ENGINEERING, vol. 171, 2023.
@article{01GTXZHEW8AQV73RJFA5NG27DK,
abstract = {{This paper presents surrogate models of dissolution behavior to identify the critical input parameters in tablet manufacturing processes. Dissolution behavior is a critical quality attribute, and is defined as the time profile of fraction of dissolved active pharmaceutical ingredients in a solvent from a tablet. Dissolution behavior was first calculated by mechanistic models using gPROMS (R) and fitted by the Weibull model. Random forest regression calculated the Weibull model parameters from the input model parameters. The developed models enabled to reduce computational cost without losing accuracy. Case studies were performed for both dry and wet granu-lation processes using paracetamol. The results identified key input and intermediate parameters to the disso-lution behavior, e.g., granule bulk density and porosity. In addition, wet granulation had a tendency to make dissolution faster than dry granulation. The proposed approach can contribute to enhance simulation capabilities in the limited time of pharmaceutical process development.}},
articleno = {{108141}},
author = {{Matsunami, Kensaku and Miura, Tomohiro and Yaginuma, Keita and Tanabe, Shuichi and Badr, Sara and Sugiyama, Hirokazu}},
issn = {{0098-1354}},
journal = {{COMPUTERS & CHEMICAL ENGINEERING}},
keywords = {{Computer Science Applications,General Chemical Engineering,Sensitivity analysis,Machine learning,Roller compaction,Wet granulation,Process design,Pharmaceutical manufacturing}},
language = {{eng}},
pages = {{12}},
title = {{Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes}},
url = {{http://doi.org/10.1016/j.compchemeng.2023.108141}},
volume = {{171}},
year = {{2023}},
}
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