Transforming Analytics To Optimize Cell Line Development

By Peggy Lio, Cytiva, and Miranda Kheradmand, Danaher Life Sciences

For cell line development (CLD), much of a workflow’s success hinges on how much analytical data a team can get – and how quickly – to inform their process. The analytical methods supporting a CLD workflow are often complex, and many of the most crucial insights are gained from instruments that are often large capital expenses, too large to bring in-house, or too challenging for most CLD personnel to operate easily.

Achieving the analytical insights that enable decision-making is a key challenge for CLD laboratories. For many organizations, the technologies and expertise needed to drive these insights requires outsourcing a majority of their analytics to an external laboratory. While CLD labs can leverage certain analytics in-house to quickly measure immunoglobulin G (IgG) titer or count cells, these techniques are used for screening of cell lines. Other critical quality attributes (CQAs), such as product glycosylation or protein charge variants, typically require outsourcing. Once clones are identified, many of the outsourced analytics needed to determine the best clones can take days to return results, delaying insights and potentially impacting other steps in a process.