SUPERVISOR: Rainer HAHN 

PROJECT ASSIGNED TO: Timon Kalchmayr

The present study investigates the separation of a multi-component protein mixture by means of adsorption on ion exchange chromatography. The objective is to ascertain buffer conditions at which the target protein is not adsorbed, while three other proteins and a small molecular weight impurity are bound. The primary objective is to optimise throughput and purity for the target protein.

The work involves a thorough screening of various chromatography resins. Adsorption is studied by establishing multicomponent adsorption isotherms, which are examined in a high-throughput manner under varying buffer conditions. In this respect, pH and conductivity will be explored within a specific range. The experimental data will be fitted using different isotherm models, which may be mechanistic or based on hybrid modelling techniques. The kinetics of adsorption will be investigated using batch adsorption techniques. The isotherm model is coupled with a model that describes protein uptake, and the performance of column adsorption is predicted. Experimental column adsorption runs will be carried out, with the data being used to fine-tune the model. The overall goal is to establish a model that can fully describe the adsorption process and be used to optimise it. Furthermore, the model should be able to predict how the performance will change if the initial protein and buffer composition changes.

Figure 1: Graphical abstract of the project “Model-assisted optimisation of multi-component protein separation”, showing the work areas and methods to be used. (Created with BioRender.com)

References

Beck J, Hochdaninger G, Carta G, Hahn R. Resin structure impacts two-component protein adsorption and separation in anion exchange chromatography. Journal of Chromatography A. 2023;1705:464208. doi:10.1016/j.chroma.2023.464208

Beck J, Hahn R, Carta G. Influence of resin structure on the prediction of two-component protein adsorption behavior in anion exchange resins from single component batch data. Journal of Chromatography A. 2026;1765:466483. doi:10.1016/j.chroma.2025.466483