Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients
Аутори
Đuriš, JelenaCirin-Varađan, Slobodanka
Aleksić, Ivana
Đuriš, Mihal
Cvijić, Sandra
Ibrić, Svetlana
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks... (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients.
Кључне речи:
Neural networks / Co-processed excipients / Compaction analysis / Lactose / Lipid excipients / Machine learning / Monohydrate / Multilayer perceptron / Sensitivity analysis / Tensile strengthИзвор:
Pharmaceutics, 2021, 13, 5Издавач:
- MDPI AG
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200026 (Универзитет у Београду, Институт за хемију, технологију и металургију - ИХТМ) (RS-200026)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200161 (Универзитет у Београду, Фармацеутски факултет) (RS-200161)
DOI: 10.3390/pharmaceutics13050663
ISSN: 1999-4923
WoS: 000662452600001
Scopus: 2-s2.0-85106191656
Институција/група
PharmacyTY - JOUR AU - Đuriš, Jelena AU - Cirin-Varađan, Slobodanka AU - Aleksić, Ivana AU - Đuriš, Mihal AU - Cvijić, Sandra AU - Ibrić, Svetlana PY - 2021 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3905 AB - Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients. PB - MDPI AG T2 - Pharmaceutics T1 - Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients VL - 13 IS - 5 DO - 10.3390/pharmaceutics13050663 ER -
@article{ author = "Đuriš, Jelena and Cirin-Varađan, Slobodanka and Aleksić, Ivana and Đuriš, Mihal and Cvijić, Sandra and Ibrić, Svetlana", year = "2021", abstract = "Co-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients.", publisher = "MDPI AG", journal = "Pharmaceutics", title = "Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients", volume = "13", number = "5", doi = "10.3390/pharmaceutics13050663" }
Đuriš, J., Cirin-Varađan, S., Aleksić, I., Đuriš, M., Cvijić, S.,& Ibrić, S.. (2021). Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients. in Pharmaceutics MDPI AG., 13(5). https://doi.org/10.3390/pharmaceutics13050663
Đuriš J, Cirin-Varađan S, Aleksić I, Đuriš M, Cvijić S, Ibrić S. Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients. in Pharmaceutics. 2021;13(5). doi:10.3390/pharmaceutics13050663 .
Đuriš, Jelena, Cirin-Varađan, Slobodanka, Aleksić, Ivana, Đuriš, Mihal, Cvijić, Sandra, Ibrić, Svetlana, "Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients" in Pharmaceutics, 13, no. 5 (2021), https://doi.org/10.3390/pharmaceutics13050663 . .