Adventitious viral clearance is a key aspect in the safe production of biologics. Here, we use mock minute virus of mice (MVM) and retrovirus-like particles (RVLP) to facilitate the identification of optimal conditions for viral clearance during mAb process development.

In this biosimilar polishing study, a range of chromatography solutions was investigated including Q chemistry in resin and membrane formats and a mixed-mode resin with anion exchange and hydrophobic functionalities. The sorbents were initially evaluated in high-throughput screening plates, followed by 1 mL, 5 mL, and 16 mL columns. Using design of experiments, we found that host cell protein clearance can be mapped with the 96-well high-throughput plate method. However, aggregate and viral clearance were more accurately predicted using column formats. Using 5 mL columns, we investigated the impact of bed height and residence time on impurity clearance. We found that these parameters have no significant impact on Q chemistry formats, but are important for aggregate and viral clearance for the mixed-mode format.

To confirm these key findings and further test the ability of these sorbents to clear virus in a “worst-case” scenario, we conducted a protein-protein interaction study. In this study, we screened five mAbs for their ability to directly bind virus particles (Fig 1). We identified a mAb that can bind MVM mock virus particles in a low-conductivity condition. We then repeated key impurity removal experiments and found that viral clearance for the worst-case mAb could still be achieved, but optimal impurity removal required higher conductivity than for the initial mAb.

Together, these studies show the value of developing a deeper understanding of viral clearance to guide process scale-up.

Fig 1. Protein-protein interaction approach to identify mAbs with virus affinity. A biosensor with bound mAb was tested with RVLP and mock MVM at low (3 mS/cm) and high (40 mS/cm) conductivities. The mAb with the strongest binding signal (green) was chosen as “worst-case scenario.”

Mark Schofield

Cytiva

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