Automated scale-up crystallisation DataFactory for model-based pharmaceutical process development: a Bayesian case study

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.