Commit: Automated Scale-Up Crystallisation DataFactory for model-based pharmaceutical process development: a data-driven, mechanistic and hybrid model case study
Year: 2026
Type: preprint
Abstract: In 2025, we introduced the Scale-Up Crystallisation DataFactory, an automated platform for crystallisation experimentation and Bayesian optimisation. However, the initial case study used a relatively simple three-parameter, single-objective optimisation problem. In this Commit paper, we extend the platform to a five-dimensional, multi-objective cooling-antisolvent crystallisation of lovastatin and benchmark data-driven, mechanistic and hybrid optimisation approaches. Sixteen screening experiments were followed by model-guided experiments targeting yield and particle size (D90). Bayesian optimisation produced continued improvements in hypervolume, while mechanistic and hybrid approaches did not improve experimental performance beyond screening. The results demonstrate the value of experimentally benchmarking optimisation strategies for autonomous process development.
Source: ChemRxiv
Authors: Thomas Pickles, Cameron J. Brown
Institution: University of Strathclyde
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221522039