Accounting for host cell protein behavior in anion-exchange chromatography.

Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1453-1463, 2016.

Molecular perspective of antibody aggregates and their adsorption on Protein A resin.

Antibody aggregate is a common issue in therapeutic antibodies, which may compromise product efficacy and cause adverse effects. Antibody aggregate level is normally controlled in bioprocessing by polishing steps after Protein A capture. This paper studied the Higher Order Structures (HOS) of antibody aggregates (dimer H1 and H2) and their adsorption on Protein A resin and thus elucidated the mechanism using Protein A capture for enhanced aggregate removal. The HOS of antibody aggregates and their complex with Protein A were characterized using HDX-MS combined with SEC-MALS, Protein Conformational Array (PCA), and molecular modeling. The aggregate size and Protein A binding ratio suggested that H2 has much more compact structure than H1. HDX-MS and PCA further revealed that H1 was formed by single Fab-Fab interaction while H2 formed by Fab-Fab and likely Fc-Fc interaction. On Protein A resin, both the molar binding ratio and the correlation between protein size and ligand distance support that each monomer can only bind one Protein A ligand, while each dimer can bind two ligands, thus resulting in stronger resin binding. Furthermore, dimer H2 binds stronger than dimer H1 due to its compact structure. By integrating biophysical analysis and molecular modeling with process development, this study revealed the antibody aggregate structures and the mechanism of aggregate removal using Protein A chromatography. It also provided a general strategy for in-depth product and process understanding in antibody and other biologics development.

Proteomics-based, multivariate random forest method for prediction of protein separation behavior during cation-exchange chromatography.

The most significant cost of recombinant protein production lies in the optimization of the downstream purification methods, mainly due to a lack of knowledge of the separation behavior of the host cell proteins (HCP). To reduce the effort required for purification process development, this work was aimed at modeling the separation behavior of a complex mixture of proteins in cation-exchange chromatography (CEX). With the emergence of molecular pharming as a viable option for the production of recombinant pharmaceutical proteins, the HCP mixture chosen was an extract of corn germ. Aqueous two phase system (ATPS) partitioning followed by two-dimensional electrophoresis (2DE) provided data on isoelectric point, molecular weight and surface hydrophobicity of the extract and step-elution fractions. A multivariate random forest (MVRF) method was then developed using the three characterization variables to predict the elution pattern of individual corn HCP. The MVRF method achieved an average root mean squared error (RMSE) value of 0.0406 (fraction of protein eluted in each CEX elution step) for all the proteins that were characterized, providing evidence for the effectiveness of both the characterization method and the analysis approach for protein purification applications.