Since the first approval of a recombinant biopharmaceutical in 1986, Chinese hamster ovary (CHO) cells have become the industry workhorse for biologics production. While there have been dramatic improvements in multiple areas of cell line development (e.g., transfection and fed-batch process), the design of the vector still typically relies on a “one-size-fits-all approach”: the therapeutic molecule’s coding sequences are inserted into a fixed plasmid, which contains a small set of legacy genetic components. This rigid approach often results in suboptimal expression for both monoclonal antibodies and complex modalities (e.g., bispecific antibodies), which can impede clinical development.

In this talk, we present the CHO Edge System, which moves beyond the one-size-fits-all paradigm by tailoring vectors for each molecule to optimize expression. The system integrates a glutamine synthetase (GS)-CRISPR knockout CHO host, a hyperactive transposase, libraries of characterized genetic elements to control cellular functions, and computational tools for rational vector design and multi-omics analysis. Collectively, these genetic and software tools have enabled: (1) development of high-copy, high-expression producer lines with long-term stability, (2) fine-tuning of heavy/light chain expression and GS selection, and (3) machine learning-based optimization for increased translational efficiency, secretion, and cell growth. We present case studies highlighting the impact of these tools to optimize expression for both standard monoclonal and bispecific antibodies.