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Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products

Keita Yaginuma (UGent) , Kensaku Matsunami (UGent) , Laure Descamps (UGent) , Alexander Ryckaert (UGent) and Thomas De Beer (UGent)
(2024) INTERNATIONAL JOURNAL OF PHARMACEUTICS. 662.
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Abstract
T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations and process conditions. However, it is usually challenging to collect comprehensive experimental data using large-scale manufacturing equipment because of the cost, time, and large consumption of raw materials. This study proposes a hybrid modeling of T-PLSR and transfer learning (TL) to enhance the prediction performance of a T-PLSR model for large-scale manufacturing data by exploiting a large amount of small-scale manufacturing data for model building. The proposed method of T-PLSR+TL was applied to a practical case study focusing on scaling up the tableting process from an experienced compaction simulator to a less-experienced rotary tablet press. The T-PLSR+TL models achieved significantly better prediction performance for tablet quality attributes of new drug products than T-PLSR models without using large-scale manufacturing data with new drug products. The results demonstrated that T-PLSR+TL is more capable of addressing new drug products than T-PLSR by using small-scale manufacturing data to cover a scarcity of large-scale manufacturing data. Furthermore, T-PLSR+TL holds the potential to streamline formulation and manufacturing process development activities for new drug products using an extensive database.
Keywords
Transfer learning, T-shaped partial least squares regression, Machine learning, Scale-up, Tableting

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MLA
Yaginuma, Keita, et al. “Hybrid Modeling of T-Shaped Partial Least Squares Regression and Transfer Learning for Formulation and Manufacturing Process Development of New Drug Products.” INTERNATIONAL JOURNAL OF PHARMACEUTICS, vol. 662, 2024, doi:10.1016/j.ijpharm.2024.124463.
APA
Yaginuma, K., Matsunami, K., Descamps, L., Ryckaert, A., & De Beer, T. (2024). Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products. INTERNATIONAL JOURNAL OF PHARMACEUTICS, 662. https://doi.org/10.1016/j.ijpharm.2024.124463
Chicago author-date
Yaginuma, Keita, Kensaku Matsunami, Laure Descamps, Alexander Ryckaert, and Thomas De Beer. 2024. “Hybrid Modeling of T-Shaped Partial Least Squares Regression and Transfer Learning for Formulation and Manufacturing Process Development of New Drug Products.” INTERNATIONAL JOURNAL OF PHARMACEUTICS 662. https://doi.org/10.1016/j.ijpharm.2024.124463.
Chicago author-date (all authors)
Yaginuma, Keita, Kensaku Matsunami, Laure Descamps, Alexander Ryckaert, and Thomas De Beer. 2024. “Hybrid Modeling of T-Shaped Partial Least Squares Regression and Transfer Learning for Formulation and Manufacturing Process Development of New Drug Products.” INTERNATIONAL JOURNAL OF PHARMACEUTICS 662. doi:10.1016/j.ijpharm.2024.124463.
Vancouver
1.
Yaginuma K, Matsunami K, Descamps L, Ryckaert A, De Beer T. Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products. INTERNATIONAL JOURNAL OF PHARMACEUTICS. 2024;662.
IEEE
[1]
K. Yaginuma, K. Matsunami, L. Descamps, A. Ryckaert, and T. De Beer, “Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products,” INTERNATIONAL JOURNAL OF PHARMACEUTICS, vol. 662, 2024.
@article{01J3D9Z8GM72HMJBNDXDMAHCY9,
  abstract     = {{T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations and process conditions. However, it is usually challenging to collect comprehensive experimental data using large-scale manufacturing equipment because of the cost, time, and large consumption of raw materials. This study proposes a hybrid modeling of T-PLSR and transfer learning (TL) to enhance the prediction performance of a T-PLSR model for large-scale manufacturing data by exploiting a large amount of small-scale manufacturing data for model building. The proposed method of T-PLSR+TL was applied to a practical case study focusing on scaling up the tableting process from an experienced compaction simulator to a less-experienced rotary tablet press. The T-PLSR+TL models achieved significantly better prediction performance for tablet quality attributes of new drug products than T-PLSR models without using large-scale manufacturing data with new drug products. The results demonstrated that T-PLSR+TL is more capable of addressing new drug products than T-PLSR by using small-scale manufacturing data to cover a scarcity of large-scale manufacturing data. Furthermore, T-PLSR+TL holds the potential to streamline formulation and manufacturing process development activities for new drug products using an extensive database.}},
  articleno    = {{124463}},
  author       = {{Yaginuma, Keita and Matsunami, Kensaku and Descamps, Laure and Ryckaert, Alexander and De Beer, Thomas}},
  issn         = {{0378-5173}},
  journal      = {{INTERNATIONAL JOURNAL OF PHARMACEUTICS}},
  keywords     = {{Transfer learning,T-shaped partial least squares regression,Machine learning,Scale-up,Tableting}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products}},
  url          = {{http://doi.org/10.1016/j.ijpharm.2024.124463}},
  volume       = {{662}},
  year         = {{2024}},
}
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