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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...

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Detalles Bibliográficos
Autores principales: Munir, Nimra, Nugent, Michael, Whitaker, Darren, McAfee, Marion
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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