Structure-Based Prediction of Protein Behavior in Preparative Chromatography

Abstract

Vaccination plays a pivotal role in modern preventive healthcare and contributes to a global decline in infectious diseases. Efficient production of vaccines is essential to meet the growing demand which results from factors like a growing global population and increased international travel. Protein subunit vaccines are a vaccine modality that contains parts of the infectious pathogen as the active ingredients. These subunits are recognized by the immune system, which is trained to respond more effectively and reduce symptoms upon actual infection. Production of these vaccines is divided into upstream processing (USP), which involves fermentation using expression hosts, downstream processing (DSP) where the protein subunit is purified, and finally formulation where the vaccines are prepared for distribution. During the DSP, multiple chromatography modes are often used to reach the required purity. Selection of the optimal chromatographic resin types, as well as operating conditions can be expensive and time consuming. Model-based process development has the potential to speed up this selection by using computational methods to predict protein behavior. Especially in early phase development, models allow in silico screening of resins and conditions in tandem to classical experiments, reducing required material. These computational models can be divided into knowledge-driven, datadriven, or a combination thereof.