The Next Phase of Cell Line Development

Cell line development timelines remain tightly governed by biology, even as new tools improve how efficiently teams work within those limits. While automation and digitalization are now embedded in many CLD workflows, expectations of dramatic time compression are giving way to a more realistic focus on optimization: generating better data earlier, making more confident clone‑selection decisions, and reducing rework and downstream risk. These themes were highlighted at a recent virtual CLD event, where Tom Kelly of Johnson & Johnson, representing BioPhorum, outlined industry‑wide constraints and opportunities for timeline optimization, and Xiaoyan Tang of Merck & Co. (MSD) shared practical experience showing how structured, automated platforms can improve predictability and decision quality.

CHEManager International: Automation is often described as the key to significantly shortening cell line development timelines. How much impact is it really having?

Tom Kelly: Automation is certainly being adopted more widely across the industry, but it’s important to be realistic about what it can and can’t do. What we’re seeing is that automation isn’t really shortening CLD timelines in a fundamental way. The biggest driver is still the doubling time of the cells. Whether someone is doing that work manually or using automated systems, you can’t make the cells grow faster.
Its biggest impact is on workload rather than absolute timelines. Automation reduces the amount of hands‑on work scientists are doing. It allows people to oversee more experiments and potentially work on more than one project at the same time. It can also support quicker decision‑making, often within the same day. But it’s not typically saving days, weeks, or certainly not months in CLD itself.