ArticlePharmaceutics2021
Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients.
Article in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of Co-Amorphous Systems for Inhalation Therapy-Part 1: From Model Prediction to Clinical Success.Pharmaceutics · 2025Article
- Evaluation of the Potential of Novel Co-Processed Excipients to Enable Direct Compression and Modified Release of Ibuprofen.Pharmaceutics · 2024Article
- Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers.Pharmaceutics · 2023Article
- Data-Driven Prediction of the Formation of Co-Amorphous Systems.Pharmaceutics · 2023Article
- Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing-a Review.The AAPS journal · 2022Review
- Improving Tableting Performance of Lactose Monohydrate by Fluid-Bed Melt Granulation Co-Processing.Pharmaceutics · 2021Article
- Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study.Pharmaceutics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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