Developing a model-driven workflow for the digital design of small-scale batch cooling crystallisation with the antiviral lamivudine

Abstract

We present a workflow that uses digital tools to optimise the experimental approach and maximise the efficiency in achieving the required process parameters for a desired set of crystallisation responses, kinetics and objectives. Model-driven small-scale experiments can contribute to reducing time and material waste in the development of pharmaceutical crystallisation processes. The workflow presented here guides the development of a small-scale batch cooling crystallisation process via solubility measurements, particle shape and size determination, form identification and preliminary kinetic parameter estimation to make crystals that satisfy quality target parameters (for shape, size and solubility) for a given active pharmaceutical ingredient (API). The case study herein follows the development of a crystallisation process for lamivudine, an API used in the preventative treatment of human immunodeficiency virus (HIV). This work identifies ethanol as a suitable solvent, meeting the acceptable solubility parameters for industrially relevant processes and yielded the biorelevant form, form I. The target kinetic parameters that were measured included induction time, growth rate and nucleation rate for lamivudine in ethanol under a range of conditions as guided by experimental planning models. Data was collected as part of the development of a DataFactory platform in which experimental optimisation can be autonomously implemented and all measurements stored in a crystallisation parameter database. This database will have further value in informing model development and continuous crystallisation process design and optimisation. The model objective-driven development workflow identified the following conditions, 19.9 °C, 600 RPM and supersaturation of 1.70, as achieving the desired objective successfully in 80 polythermal and 28 isothermal experiments. Integration of the workflow alongside the optimisation algorithm within the automated DataFactory system will enable fully autonomous, rapid data collection for small-scale API crystallisation. Such autonomous systems could play vital roles in pharmaceutical development and manufacturing driving towards more efficient and sustainable practices via digital transformation.