Bioprocess decision support tool for scalable manufacture of extracellular vesicles
Name
bit.26809.pdf
Description
Published version
Size
3.04 MB
Format
Unknown
Checksum (MD5)
1cf9f4f37ded78b049320d2359cb41c3
Author(s)
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.
Date Issued
November 2018
Journal
Biotechnology and Bioengineering
Publisher
Wiley
Citation
Ng, KS, Smith, JA, McAteer, MP, Mead, BE, Ware, J et al. 2019. "Bioprocess decision support tool for scalable manufacture of extracellular vesicles." Biotechnology and Bioengineering, 116 (2).
Version
Final published version
Abstract
© 2018 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. 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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/bit.26809
