Modelling and simulation of pharmaceutical processes

Design, scale-up and optimization of crystallization processes

Improved crystal size distribution (CSD)

You can use the understanding gained from modelling to optimise crystal sizes and crystal size distribution

See The pharmaceutical crystallisation case in the crystallisation section.

Crystallisation is fundamental to pharmaceutical operation, with some 70% of products going through a crystallisation step at some point in their manufacture.

Typically pharmaceutical-grade crystalline products require a narrow particle size distribution, which implies that the primary production process must be well-designed and tightly controlled under optimal conditions.

PSE's Advanced Model Library for Solution Crystallisation (AML:SC) comprises state-of-the-art population balance and kinetic models for modelling of batch, semi-batch and continuous pharmaceutical crystallisation processes.

It also provides extensive facilities for determining of model paramaters from experimental data using model-based data analysis techniques.

By applying crystallisation experience gained in modelling crystallisation processes in the chemicals and food sectors, PSE is helping to bring new confidence to the design and scale-up of primary pharma processes that have traditionally posed challenges. An example is the manufacture of high-purity lactose.

Benefits of modelling crystallisation systems

Using an Advanced Process Model of the crystallser and crystallisation process, it is possible to:

Scale-up with confidence, leading to right-first-time commercial designs
Improve primary production, leading to higher throughput and efficiency in secondary production
Ensure crystal product quality (size and size distribution) through determining of optimal production conditions
Determine optimum seeding policy and growth rates through rigorous analysis
Determine accurate kinetics of key crystallisation phenomena
Validate models from experimental data, ensuring accurate predictive models and making the most of available experimental data
Guide experimental programmes using model-based data analysis and experiment design techniques, to ensure maximum accuracy of models witth minimum experimentation
Reduce batch times by determining optimal heating/cooling and seeding policies
Improve reliability of processes by ensuring that results are reproducible every time
Develop a new quantitative understanding of processes leading to greater production flexibility based on informed decisions in the future.

See the general benefits of modelling crystallisation systems for more information.

How PSE can help

PSE's ModelCare service can provide you with a high-accuracy predictive model of your crystallisation process, incorporating your own laboratory or pilot data where available. We can also advise on optimal experimental programmes.

We can then apply, or help you to apply, optimisation techniques to maximise quality, throughput and reproducability while minimising batch times.