Peer-Reviewed Journal Details
Mandatory Fields
Munir, Nimra and Nugent, Michael and Whitaker, Darren and McAfee, Marion
Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions
Optional Fields
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.
Grant Details