(84c) Model-Based Decision Support for Design and Operation of Pharmaceutical Crystallisation Processes: Efficient Workflows for Validation Against Experiments and Scale-up

This paper describes GSK's assessment of available tools and techniques for model-based design and optimisation of crystallisation processes and considers the potential for using the same models to quantify the design space of these processes.  In order for model-based decision support to be of practical value to the pharmaceutical industry, it is essential to have workflows that allow model development, experimentation, parameter estimation, process optimisation and scale-up to be done in a period of 4-8 weeks.

The batch cooling crystallisation of an API from a solvent was selected as a case study.  The process was seeded and investigation of crystal images revealed that the dominant mechanisms are growth, attrition and to a lesser extent agglomeration.  The key challenges from the process development perspective are to be able to predict attrition and agglomeration as a function of operating conditions, crystalliser/agitator type and equipment scale.

A number of experiments were conducted to investigate the final PSD and solute concentration over time as a function of seeding conditions (amount and PSD of the seeds) and cooling profile.  Slurry samples were taken at critical time points.  Solid samples were isolated by vacuum filtration and analysed for PSD by Malvern Mastersizer, whilst assay was performed on the liquid filtrate to measure residual solute concentration.

A model of the batch cooling crystallisation set-up used for the experimental work was developed using a commercially available tool.  The developed model is based on a population balance framework that supports both steady-state and dynamic applications.  For the model validation, the unknown kinetic parameters were estimated against the solute concentration and PSD measurements of the performed experiments.  This was done for all experiments simultaneously.

The validated model was subsequently combined with flow information from a CFD model to construct a so-called Multizonal model (see Figure below) that allowed prediction of local wall temperatures, primary and secondary nucleation rates, growth (and dissolution) rates as well as agglomeration rates.  This enables scale-up on a sound physical basis.

The value of a model-based approach to process development is to reduce the number of experiments required to understand and predict the crystallisation behaviour.  The approach used here allows one to predict the behaviour at the same scale (e.g. to troubleshoot an existing process or to reduce batch time whilst satisfying PSD and purity constraints) as well as at different scales or different configurations at the same/similar scale (e.g. to aid scale-up).