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Bioprocess decision support tool for scalable manufacture of extracellular vesicles

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  • Additional Information
    • Contributors:
      Harvard University--MIT Division of Health Sciences and Technology; Koch Institute for Integrative Cancer Research at MIT
    • Publication Information:
      Wiley
    • Publication Date:
      2022
    • Collection:
      DSpace@MIT (Massachusetts Institute of Technology)
    • 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.
    • File Description:
      application/octet-stream
    • Relation:
      http://dx.doi.org/10.1002/bit.26809; Biotechnology and Bioengineering; https://hdl.handle.net/1721.1/141249.2; 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).
    • Online Access:
      https://hdl.handle.net/1721.1/141249.2
    • Rights:
      Creative Commons Attribution 4.0 International license ; https://creativecommons.org/licenses/by/4.0/
    • Accession Number:
      edsbas.75CC5BCA