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Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions
In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug A...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466632/ https://www.ncbi.nlm.nih.gov/pubmed/34575508 http://dx.doi.org/10.3390/pharmaceutics13091432 |
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author | Munir, Nimra Nugent, Michael Whitaker, Darren McAfee, Marion |
author_facet | Munir, Nimra Nugent, Michael Whitaker, Darren McAfee, Marion |
author_sort | Munir, Nimra |
collection | PubMed |
description | In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry. |
format | Online Article Text |
id | pubmed-8466632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84666322021-09-27 Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions Munir, Nimra Nugent, Michael Whitaker, Darren McAfee, Marion Pharmaceutics Review In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry. MDPI 2021-09-09 /pmc/articles/PMC8466632/ /pubmed/34575508 http://dx.doi.org/10.3390/pharmaceutics13091432 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Munir, Nimra Nugent, Michael Whitaker, Darren McAfee, Marion Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions |
title | Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions |
title_full | Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions |
title_fullStr | Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions |
title_full_unstemmed | Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions |
title_short | Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions |
title_sort | machine learning for process monitoring and control of hot-melt extrusion: current state of the art and future directions |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466632/ https://www.ncbi.nlm.nih.gov/pubmed/34575508 http://dx.doi.org/10.3390/pharmaceutics13091432 |
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