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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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Determination of the dispersion characteristics/or morphology of additives in polymer melts by fast, reliable and accurate on-line methods is highly desired in the polymer industry. An ultraviolet-visible (UV-Vis) spectroscopic methodology is described which meets these demands. It is demonstrated that the applied methodology may be developed on a cheap, packaging grade of Polylactic Acid (PLLA), an important bioresorbable polymer for the medical device industry, and still be accurate when implemented on a production line using a more expensive (medical) grade of the polymer compound. Simple chemometric algorithms are applied allowing the data processing step to be carried out in near real time, thus providing vital information to process operators which allows any out of control process to be identified and rectified without product loss.