TY - JOUR
T1 - Machine learning for process monitoring and control of hot-melt extrusion
T2 - Current state of the art and future directions
AU - Munir, Nimra
AU - Nugent, Michael
AU - Whitaker, Darren
AU - McAfee, Marion
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Drug
KW - Hot-melt extrusion (HME)
KW - In/on-line process monitoring
KW - Industry 4.0
KW - Machine learning
KW - Polymer
KW - Process analytical technology
UR - http://www.scopus.com/inward/record.url?scp=85115005020&partnerID=8YFLogxK
U2 - 10.3390/pharmaceutics13091432
DO - 10.3390/pharmaceutics13091432
M3 - Review article
AN - SCOPUS:85115005020
SN - 1999-4923
VL - 13
JO - Pharmaceutics
JF - Pharmaceutics
IS - 9
M1 - 1432
ER -