Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients
Authors
Đuriš, Jelena
Cirin-Varađan, Slobodanka
Aleksić, Ivana

Đuriš, Mihal

Cvijić, Sandra

Ibrić, Svetlana

Article (Published version)
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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.
Keywords:
Co-processed excipients / Compaction analysis / Lactose / Lipid excipients / Machine learning / Monohydrate / Multilayer perceptron / Neural networks / Sensitivity analysis / Tensile strengthSource:
Pharmaceutics, 2021, 13, 5, 663-Publisher:
- MDPI
Funding / projects:
- Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200161 (University of Belgrade, Faculty of Pharmacy) (RS-200161)
- Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200026 (University of Belgrade, Institute of Chemistry, Technology and Metallurgy - IChTM) (RS-200026)
DOI: 10.3390/pharmaceutics13050663
ISSN: 1999-4923
PubMed: 34063158
WoS: 000662452600001
Scopus: 2-s2.0-85106191656
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IHTMTY - JOUR AU - Đuriš, Jelena AU - Cirin-Varađan, Slobodanka AU - Aleksić, Ivana AU - Đuriš, Mihal AU - Cvijić, Sandra AU - Ibrić, Svetlana PY - 2021 UR - https://cer.ihtm.bg.ac.rs/handle/123456789/4693 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 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 SP - 663 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", 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", pages = "663", 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., 13(5), 663. 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):663. 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):663, https://doi.org/10.3390/pharmaceutics13050663 . .