Bioprocess decision support tool for scalable manufacture of extracellular vesicles. Issue 2 (8th November 2018)

• Record Type: Journal Article
• Authors: Ng, Kelvin S.; Smith, James A.; McAteer, Matthew P.; Mead, Benjamin E.; Ware, Jamie; Jackson, Felix O.; Carter, Alison; Ferreira, Lino; Bure, Kim; Rowley, Jon A.; Reeve, Brock; Brindley, David A.; Karp, Jeffrey M.
• Is Part Of: Biotechnology and bioengineering. Volume 116:Issue 2(2019)
• Page Start: 307
• Page End: 319
• Publication Date: 2018-11-08
• Subjects: costs -- economics -- exosomes -- extracellular vesicles -- manufacturing -- scale‐up; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.26809
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: Newly recognized as natural nanocarriers that deliver biological information between cells, extracellular vesicles (EVs), including exosomes and microvesicles, provide unprecedented therapeutic opportunities. Large‐scale and cost‐effective manufacturing is imperative for EV products to meet commercial and clinical demands; successful translation requires careful decisions that minimize financial and technological risks. Here, we develop a decision support tool (DST) that computes the most cost‐effective technologies for manufacturing EVs at different scales, by examining the costs of goods associated with using published protocols. The DST identifies costs of labor and consumables during EV harvest as key cost drivers, substantiating a need for larger‐scale, higher‐throughput, and automated technologies for harvesting EVs. Importantly, we highlight a lack of appropriate technologies for meeting clinical demands, and propose a potentially cost‐effective solution. This DST can facilitate decision‐making very early on in development and be used to predict, and better manage, the risk of process changes when commercializing EV products. Abstract : Large‐scale and cost‐effective manufacturing is imperative for EV products to meet commercial and clinical demands; successful translation requires careful decisions that minimize financial and technological risks. Here, we develop a decision support tool (DST) that computes the most cost‐effective technologies for manufacturing EVs at different scales, by examining the costs of goods associated with using published protocols.