Application of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients
| Autor: | Mihal Djuris, Slobodanka Cirin-Varadjan, Ivana Aleksic, Jelena Djuris, Svetlana Ibrić, Sandra Cvijić |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Compaction co-processed excipients Pharmaceutical Science Lactose 02 engineering and technology 030226 pharmacology & pharmacy Dosage form Article Tensile strength lactose 03 medical and health sciences Tableting 0302 clinical medicine Pharmacy and materia medica sensitivity analysis Ultimate tensile strength monohydrate Machine learning Multilayer perceptron multilayer perceptron Process engineering Lipid excipients Monohydrate Active ingredient Artificial neural network business.industry 021001 nanoscience & nanotechnology Compression (physics) neural networks compaction analysis RS1-441 lipid excipients machine learning tensile strength Co-processed excipients Compaction analysis 0210 nano-technology business Sensitivity analysis Neural networks |
| Zdroj: | Pharmaceutics Volume 13 Issue 5 Pharmaceutics, Vol 13, Iss 663, p 663 (2021) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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