Automated scale-up crystallisation DataFactory for model-based pharmaceutical process development : a Bayesian case study
Pickles, Thomas and Leghrib, Youcef and Weisshaar, Matt and Gonacharuk, Mikhail and Timperman, Peter and Doherty, Timothy and Ford, David D. and Moores, Jonathan and Florence, Alastair J. and Brown, Cameron J. (2025) Automated scale-up crystallisation DataFactory for model-based pharmaceutical process development : a Bayesian case study. Digital Discovery, 4 (8). pp. 2025-2032. ISSN 2635-098X (https://doi.org/10.1039/D4DD00406J)
Preview |
Text.
Filename: Pickles-etal-DD-2025-Automated-scale-up-crystallisation-DataFactory-for-model-based-pharmaceutical.pdf
Final Published Version License:
Download (815kB)| Preview |
Abstract
Automated model-based design of experiments (MB-DoE) play an important role in enhancing process development efficiencies by minimising material usage and saving significant human labour time. This study describes the conception, installation and application of an automated platform and a model-based design of experiments approach to both plan and automate the experimental load for scale-up crystallisation process development. The platform hardware in detail is a multi-vessel configuration equipped with peristaltic pump transfer, integrated HPLC, image-based process analytical technology and single board computer control based IoT system. To demonstrate the DataFactory’s experimental capabilities a 5-point Latin hypercube design was employed to investigate the effects of cooling rate, seed mass, and seed point supersaturation on nucleation, growth, and yield during the cooling crystallisation of lamivudine in ethanol. This initial screening data served as inputs for Bayesian optimisation to determine the optimal next experiment aimed at achieving the target process parameters and reducing uncertainty. This data-driven MB-DoE approach simplifies application, provides flexibility, and accelerates experimental design, achieving a ~10% improvement in the objective function value within just 1 iteration. This study will inform future research comparing the suitability of data-driven, mechanistic, and hybrid models across various crystallisation modes.
ORCID iDs
Pickles, Thomas
ORCID: https://orcid.org/0009-0006-3377-3124, Leghrib, Youcef, Weisshaar, Matt, Gonacharuk, Mikhail, Timperman, Peter, Doherty, Timothy, Ford, David D., Moores, Jonathan, Florence, Alastair J.
ORCID: https://orcid.org/0000-0002-9706-8364 and Brown, Cameron J.
ORCID: https://orcid.org/0000-0001-7091-1721;
-
-
Item type: Article ID code: 93089 Dates: DateEvent6 August 2025Published10 June 2025Published Online1 June 2025AcceptedSubjects: Medicine > Pharmacy and materia medica > Pharmaceutical chemistry Department: Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
Strategic Research Themes > Advanced Manufacturing and Materials
Technology and Innovation Centre > Continuous Manufacturing and Crystallisation (CMAC)Depositing user: Pure Administrator Date deposited: 11 Jun 2025 13:45 Last modified: 13 Aug 2026 10:11 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/93089
Tools
Tools






