Improving data management for modern Biologics process development

For Cue Biopharma, it was challenging to figure out how to track all their data and full experimental context — in addition to insights to inform the engineering process.

Rapid scaling, combined with the inherent difficulty of bridging research and development, led to communication and handoff challenges.

Traditional data management systems, such as LIMS, have data structures that are fairly static, preventing novel ideas and ingenuity from being incorporated into data models.

Cue Biopharma has created a strong foundation for data capture within Benchling. With the name, location, history, and results of all samples now stored in an interconnected suite of applications, scientists experience faster, more complete handoffs across teams with a unified solution. By bringing together capabilities for process design, execution, and analysis, Benchling enables Cue Biopharma to shorten process development cycles while harnessing data at scale. 

“From a process development standpoint, all the data we are capturing can still be in the same environment as our discovery data allowing for a  seamless knowledge transfer.  We have the benefit of various functional teams working within an environment they are already familiar with. It facilitates knowledge transfer and helps with the ability to expeditiously move the process forward.” - Samantha Povlich, Director of Analytical Operations and CMC Digital Strategy at Cue Biopharma 

Benchling helps teams across Cue Biopharma partner more effectively to coordinate samples and data transfers. With over 4000 engineered constructs to consider, it has been critical for an efficient operation to have an interconnected system automatically managing each completed step, batching together handoffs to the next team, and recording sample history. This more streamlined request system reduces time spent asking for status updates while surfacing result trends over time.

Benchling provides Cue Biopharma the ability to modify on the fly without needing to rewrite the entire data model. By easily absorbing data from different modalities into the data structure, teams are able to capture the same information within the same environment, saving them time in learning something new. By developing databases with appropriate structures, Cue Biopharma makes data from a variety of sources easily accessible to partners, such as contract testing labs.