Bioprocess decision support tool for scalable manufacture of extracellular vesicles

被引:28
作者
Ng, Kelvin S. [1 ,2 ,3 ,4 ]
Smith, James A. [5 ,6 ]
McAteer, Matthew P. [7 ]
Mead, Benjamin E. [1 ,2 ,3 ,8 ,9 ]
Ware, Jamie [6 ]
Jackson, Felix O. [6 ]
Carter, Alison [11 ]
Ferreira, Lino [10 ]
Bure, Kim [6 ]
Rowley, Jon A. [4 ]
Reeve, Brock [3 ]
Brindley, David A. [3 ,6 ,11 ,12 ,13 ]
Karp, Jeffrey M. [1 ,2 ,3 ,8 ]
机构
[1] Harvard Mit Div Hlth Sci & Technol, Cambridge, MA USA
[2] Harvard Med Sch, Brigham & Womens Hosp, Dept Med, Div Engn Med, Boston, MA USA
[3] Harvard Stem Cell Inst, Cambridge, MA 02138 USA
[4] RoosterBio, Frederick, MD USA
[5] Univ Oxford, Nuffield Dept Orthopaed Rheumatol & Musculoskelet, Oxford, England
[6] Univ Oxford, Oxford UCL Ctr Adv Sustainable Med Innovat, Oxford, England
[7] Harvard Med Sch, Massachusetts Gen Hosp, Dept Neurol, Charlestown, MA USA
[8] Broad Inst Harvard & MIT, Cambridge, MA USA
[9] MIT, Koch Inst Integrat Canc Res, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[10] Univ Coimbra, Ctr Neurosci & Cell Biol, Coimbra, Portugal
[11] Univ Oxford, Dept Paediat, Oxford, England
[12] UCL, UCL Sch Pharm, Ctr Behav Med, London, England
[13] UCSF, Stanford Ctr Excellence Regulatory Sci & Innovat, San Francisco, CA USA
基金
美国国家科学基金会; 美国国家卫生研究院; 英国医学研究理事会;
关键词
costs; economics; exosomes; extracellular vesicles; manufacturing; scale-up; CELL EXPANSION; OPTIMIZATION; DESIGN; ECONOMICS; DELIVERY; IMPACT; YIELD;
D O I
10.1002/bit.26809
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 [微生物学]; 090105 [作物生产系统与生态工程];
摘要
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.
引用
收藏
页码:307 / 319
页数:13
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