Automated scale-up crystallisation DataFactory for model-based pharmaceutical process development: a Bayesian case study
This study presents the development and use of an automated model-based design of experiments platform for crystallisation process scale-up, integrating IoT-enabled hardware, in-line analytics, and a data-driven experimental planning approach.
Authors
- Alastair J. Florence (ORCID: https://orcid.org/0000-0002-9706-8364)
- Marina V. Goncharuk (ORCID: https://orcid.org/0000-0002-6984-7125)
- Cameron John Brown (ORCID: https://orcid.org/0000-0001-7091-1721)
- Jonathan Moores
- David D. Ford (ORCID: https://orcid.org/0000-0002-3947-3978)
- Thomas Pickles (ORCID: https://orcid.org/0009-0006-3377-3124)
- Timothy J. Doherty
- Youcef Leghrib
- Matt Weisshaar
- Peter Timperman
Institutions
- Engineering and Physical Sciences Research Council (GB)
- University of Strathclyde (GB)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4dd00406j
- Citations
- 1
- Primary Topic
- Crystallization and Solubility Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- Roche
- Sanofi
- Chiesi Farmaceutici
- Takeda Pharmaceuticals U.S.A.
- UCB
- Scottish Funding Council
- Engineering and Physical Sciences Research Council
- Research England