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Biotechnol Bioeng. 2019 Feb;116(2):307-319. doi: 10.1002/bit.26809. Epub 2018 Nov 8.

Bioprocess decision support tool for scalable manufacture of extracellular vesicles.

Ng KS1,2,3,4, Smith JA5,6, McAteer MP7, Mead BE1,2,3,8,9, Ware J6, Jackson FO6, Carter A10, Ferreira L11, Bure K6, Rowley JA4, Reeve B3, Brindley DA3,6,10,12,13, Karp JM1,2,3,8.

Author information

1
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts.
2
Division of Engineering in Medicine, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
3
Harvard Stem Cell Institute, Cambridge, Massachusetts.
4
RoosterBio, Frederick, Maryland.
5
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
6
The Oxford-UCL Centre for the Advancement of Sustainable Medical Innovation, University of Oxford, Oxford, UK.
7
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts.
8
Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
9
Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts.
10
Department of Paediatrics, University of Oxford, Oxford, UK.
11
University of Coimbra, Center for Neuroscience and Cell Biology, Portugal.
12
Centre for Behavioural Medicine, UCL School of Pharmacy, University College London, London, UK.
13
UCSF-Stanford Center of Excellence in Regulatory Science and Innovation, San Francisco, California.

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.

KEYWORDS:

costs; economics; exosomes; extracellular vesicles; manufacturing; scale-up

PMID:
30063243
PMCID:
PMC6322973
DOI:
10.1002/bit.26809
[Indexed for MEDLINE]
Free PMC Article

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