Rapid development and deployment of high‐volume vaccines for pandemic response
Open accessAbstract
Overcoming pandemics, such as the current Covid‐19 outbreak, requires the manufacture of several billion doses of vaccines within months. This is an extremely challenging task given the constraints in small‐scale manufacturing for clinical trials, clinical testing timelines involving multiple phases and large‐scale drug substance and drug product manufacturing. To tackle these challenges, regulatory processes are fast‐tracked, and rapid‐response manufacturing platform technologies are used. Here, we evaluate the current progress, challenges ahead and potential solutions for providing vaccines for pandemic response at an unprecedented scale and rate. Emerging rapid‐response vaccine platform technologies, especially RNA platforms, offer a high productivity estimated at over 1 billion doses per year with a small manufacturing footprint and low capital cost facilities. The self‐amplifying RNA (saRNA) drug product cost is estimated at below 1 USD/dose. These manufacturing processes and facilities can be decentralized to facilitate production, distribution, but also raw material supply. The RNA platform technology can be complemented by an a priori Quality by Design analysis aided by computational modeling in order to assure product quality and further speed up the regulatory approval processes when these platforms are used for epidemic or pandemic response in the future.
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A 5 litre bioreactor can produce almost a million mRNA vaccine doses in a single reaction.
“For example, a 5 litre bioreactor can produce almost a million mRNA vaccine doses in a single reaction6.”
From mRNA vaccines for infectious diseases: principles, delivery and clinical translation (2021) · cites this paper as [6]
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Additional file 3 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563591
Additional file 9 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563612
Additional file 5 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563597
Additional file 6 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563603
Additional file 8 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563609
Additional file 4 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563594
Additional file 7 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563606
Additional file 1 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563585
Additional file 2 of T7Max transcription system
Deich, C., Cash, B., Sato, W., Sharon, J., Aufdembrink, L. M., Gaut, N. J., Heili, J. M., Stokes, K., Engelhart, A. E., & Adamala, K. P. (2024) · Figshare · doi:10.6084/m9.figshare.26563588
Sarkis, M., Shah, N., & Papathanasiou, M. M. (2025) · Journal of Advanced Manufacturing and Processing · doi:10.1002/amp2.70049
Self-citation
- Authors
- Kis, Z., Kontoravdi, C., Dey, A. K., Shattock, R., & Shah, N.
- Affiliations
- Imperial College London (GB)
- International AIDS Vaccine Initiative (US)
- Corresponding author
- Kis, Zoltán
- Kontoravdi, Cleo
- Venue
- Journal of Advanced Manufacturing and Processing
- Publisher
- Wiley
- Published
- 2020-06-29
- Volume
- 2
- Issue
- 3
- Pages
- e10060-e10060
- ISSN
- 2637-403X (ISSN-L)
- PMC
- PMC7361221
- OpenAlex
- W3038123760
- Cited by
- 105
- References
- 32
- Citations by year
- 2026: 14 · 2025: 12 · 2024: 33 · 2023: 13 · 2022: 15 · 2021: 15 · 2020: 3
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Advanced Manufacturing and Processing (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/R013764/1)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Viral Infectious Diseases and Gene Expression in Insects
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Keywords
- pandemic response, COVID-19 vaccines, self-amplifying RNA, distributed manufacturing, quality by design, computational modeling, vaccine distribution
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