(586g) Process Optimisation of Chromatography Units Using First Principles Modelling Techniques
- Conference: AIChE Annual Meeting
- Year: 2013
- Proceeding: 2013 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
- Time: Wednesday, November 6, 2013 - 6:00pm-8:00pm
Purification is an important stage in the production of therapeutic proteins. It removes impurities and ensures quality of the product, one of the main stages in purification is chromatography. Ion exchange chromatography separates molecules based on their charge; there are two types of ion exchange chromatography; anion and cation. To develop a new biopharmaceutical process there is a lot of experimentation required to investigate it; this can be both time consuming and costly. This project looks into developing modelling tools which can be used to speed up the development of new processes.
A model was created which uses a first principles approach, where the molecular properties and the thermodynamics of the system are used to characterise it. The model uses continuity equations and the Langmuir isotherm to describe the action of adsorption and provide a tool for showing the breakthrough curve of the target molecule.
The model created required minimal experimentation to determine model parameters, PreDictor plates are used to estimate values for variables such as the equilibrium dissociation constant and the maximum binding capacity at different operating conditions. For this preliminary study pH was maintained at 7.5 so that only resin volume was tested. For this project data was collected from both the flowthrough and the elution stages to determine the difference between the two stages.
Other parameters that were required such as the coefficient of diffusion, were determined using thermodynamics so that the values calculated would not be specific to the process under certain conditions but would be adaptable to change under different conditions and different proteins.
To validate the model operational considerations such as pH, conductivity and protein load were considered to see how the model coped with changes to these variables. The data generated also allowed for the empirical information to be used to improve the model.
To investigate the effects of these variables design of experiments was used to obtain the maximum information and be able to define a design space for the specific molecule. It is aimed that this model alongside subsequent models can be used to promote quality be design, in that a new process can be understood prior to experimentation.