Model-based optimization for an industrial cation-exchange step to purify antibodies
Antibody purification is commonly based on platform processes. This guarantees both a fast process development and a reduction of the time to market. At the same time, platform based approaches do not exploit the full potential of optimization. To address this issue, modeling tools are gaining more and more interest.
Unknown feed compositions complicate the optimization. Industrial downstream processes typically show very heterogeneous mixtures like antibody compounds or entirely unknown feed compositions. Both complexity and uncertainties impede modeling approaches strongly, since they increase the number of unknown model parameters.
Inverse method to determine model parameters. With steadily high time to market pressure, screening a large parameter space is not practicable. In the present case study unknown model parameters could successfully be estimated from few column experiments and fraction analyses. By means of an inverse peak fitting approach, ChromX systematically altered the model parameters until the model prediction matched the measured chromatograms. Hereto, no prior knowledge of molar feed concentrations was necessary, but UV signals could be utilized instead. The inverse method allowed to estimate an arbitrary number of unknown parameters at the same time. This way, even crude feedstocks and complex compounds could be identified an incorporated into the model.
Time and material-efficient process optimization. Once the model was calibrated from few UV-data, it can be utilized for a further process optimization or characterization. In the present case study, a model-based optimization was then applied to identify optimal process conditions.