Optimization of random PEGylation reactions by means of high throughput screening

ABSTRACT Since the first FDA approval of a PEGylated product in 1990, so called random PEGylation reactions are still used to increase the efficacy of biopharmaceuticals and represent the major technology of all approved PEG‐modified drugs. However, the great influence of process parameters on PEGylation degree and the PEG‐binding site results in a lack of reaction specificity which can have severe impact on the product profile. Consequently, reproducible and well characterized processes are essential to meet increasing regulative requirements resulting from the quality‐by‐design (QbD) initiative, especially for this kind of modification type. In this study we present a general approach which combines the simple chemistry of random PEGylation reactions with high throughput experimentation (HTE) to achieve a well‐defined process. Robotic based batch experiments have been established in a 96‐well plate format and were analyzed to investigate the influence of different PEGylation conditions for lysozyme as model protein. With common SEC analytics highly reproducible reaction kinetics were measured and a significant influence of PEG‐excess, buffer pH, and reaction time could be investigated. Additional mono‐PEG‐lysozyme analytics showed the impact of varying buffer pH on the isoform distribution, which allowed us to identify optimal process parameters to get a maximum concentration of each isoform. Employing Micrococcus lysodeikticus based activity assays, PEG‐lysozyme33 was identified to be the isoform with the highest residual activity, followed by PEG‐lysozyme1. Based on these results, a control space for a PEGylation reaction was defined with respect to an optimal overall volumetric activity of mono‐PEG‐lysozyme isoform mixtures. Biotechnol. Bioeng. 2014;111: 104–114. © 2013 Wiley Periodicals, Inc. In order to define optimal reaction conditions for a random PEGylation process, the influence of process parameters on kinetics and the isoform formation were evaluated. For this, high sensitive isoform analytics combined with automated robotic screenings were applied, representing a classical QbD approach.