Learning from the future: towards continuous manufacturing of nanomaterials.
Saved in:
| Title: | Learning from the future: towards continuous manufacturing of nanomaterials. |
|---|---|
| Authors: | VandenBerg, Michael A., Dong, Xiangyi, Smith, William C., Tian, Geng, Stephens, Olen, O'Connor, Thomas F., Xu, Xiaoming |
| Source: | AAPS Open; 3/17/2025, Vol. 11 Issue 1, p1-25, 25p |
| Subject Terms: | TURBULENT jets (Fluid dynamics), PHYSICAL & theoretical chemistry, ARTIFICIAL intelligence, REGULATORY approval, MANUFACTURING processes, TECHNOLOGY convergence, SOLID dosage forms |
| Abstract: | The rise of continuous manufacturing (CM) in the pharmaceutical industry – particularly for the solid oral dosage form—marks a major shift in how drugs are made. Over the past decade, the adoption of CM has been fueled by notable reductions in operation costs and shorter regulatory approval timelines, setting the stage for applying CM to a wide range of drug products. Nanomaterial-containing drug products, typically liquid injectables composed of vesicles, particles, or globules, are strong candidates for future CM applications. The convergence of manufacturing technology with nanotechnology is already in progress; the rapid development and commercialization of lipid nanoparticle-based mRNA products during the pandemic exemplifies this synergy. While this success highlights the potential for rapid translation of scientific advancement into life-saving drugs, it also reveals limitations in our current capacity to scale production quickly and adapt to new therapeutic modalities. This situation underscores the urgent need for improvements in agile manufacturing technologies. Moreover, more frequent drug shortages, often stemming from quality issues and limitations in scaling up manufacturing processes, underscore the need for enhanced manufacturing capabilities to better respond to fluctuating market demand and patient needs. In this context, we will summarize some of emerging CM technologies for nanomaterials, explore the underlying principles, and discuss the untapped potential for modeling and simulation to inform the design and implementation of CM. [ABSTRACT FROM AUTHOR] |
| Copyright of AAPS Open is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Biomedical Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!