AI Article Synopsis
- Hot-melt extrusion (HME) has gained popularity in the pharmaceutical industry for its advantages in drug delivery system fabrication.
- The FDA's 'quality by design' approach has spurred research on using process analytical technology (PAT) like NIR, Raman, and UV-Vis in conjunction with machine learning for real-time monitoring of HME.
- This review evaluates the role of machine learning in pharmaceutical HME processes, addressing current challenges and outlining future directions for improvement in the field.
Article Abstract
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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466632 | PMC |
| http://dx.doi.org/10.3390/pharmaceutics13091432 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
PhyloMix: Enhancing microbiome-trait association prediction through phylogeny-mixing augmentation.
Bioinformatics
January 2025
Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
Article Synopsis
- Understanding the associations between traits and microbial composition is critical for microbiome research, but machine learning models often struggle due to the data's high-dimensional, compositional, and imbalanced nature.
- To tackle these challenges, a new data augmentation method called PhyloMix has been developed, which uses phylogenetic relationships to generate synthetic microbial samples that enhance model performance.
- PhyloMix significantly outperforms other data augmentation techniques and is effective in both supervised learning and contrastive representation learning, demonstrating its broad applicability in microbiome studies.
Nutritional intelligence in the food system: Combining food, health, data and AI expertise.
Nutr Bull
January 2025
Queen's University Belfast, Belfast, UK.
Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system.
View Article and Find Full Text PDFMFMGP: an integrated machine learning fusion model for genomic prediction.
Plant Biotechnol J
January 2025
College of Agronomy, Anhui Agricultural University, Hefei, Anhui, China.
Developing a decision support tool to predict delayed discharge from hospitals using machine learning.
BMC Health Serv Res
January 2025
Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.
View Article and Find Full Text PDFDevelopment of a model for measuring sagittal plane parameters in 10-18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology.
J Orthop Surg Res
January 2025
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Purpose: The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!