Optimizing the downstream purification process (DSP) remains a significant challenge when using biological expression hosts for the production of recombinant proteins, mainly due to a lack of knowledge of the separation behavior of the host cell proteins (HCP). The process of selecting the DSP method(s) as well as the host cell can benefit from an accurate prediction of the HCP separation behavior. Therefore, to reduce the effort required for DSP development, this work was aimed at developing a predictive statistical method for the separation behavior of a complex mixture of proteins during commonly used chromatographic and non-chromatographic methods. Aqueous two phase partitioning (ATPS) followed by two-dimensional electrophoresis (2DE) provided data on the three physicochemical properties most commonly exploited during DSP for each HCP; pI (isoelectric point), molecular weight and surface hydrophobicity. A multivariate random forest (MVRF) statistical methodology was then applied to this database of characterized proteins creating an accurate tool for predicting the separation behavior of a mixture of proteins during three commonly selected purification methods; ion exchange chromatography (IEX), hydrophobic interaction chromatography (HIC) and ammonium sulfate precipitation (ASP). The separation behaviors of two separate biological expression host extracts (corn and E. coli) were characterized and the MVRF prediction results from each will be presented. Finally, future capabilities of this work will be discussed including the potential for testing the prediction accuracy of the MVRF methodology during a simulated, multi-step DSP (i.e. ASP followed by HIC followed by IEX) allowing for the possibility of optimizing a DSP without requiring any initial product or time spent screening potential methods in the lab.

Proteomics-based, multivariate, random forest method for prediction of protein separation behavior during downstream purification

245th National Meeting (2013)

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