The discovery and optimization of any new biotherapeutic is just a starting point in the path to improving patient healthcare. Cell line development scientists must find a way to produce these compounds in sufficiently high quantities, all while ensuring that the quality and stability of a product is acceptable for use in human clinical trials.
Given the wide array of protein therapeutics being produced today, meeting tight production timelines is more difficult than ever. One way to solve these challenges is through the process of rational cell line development.
“With the increasing diversity in antibodies and other protein therapeutics, cell culture scientists often face considerable challenges when trying to balance important outcomes including the quality, stability, and yield of their target protein,” says Michael Johnson, PhD, a scientific account manager at Genedata, headquartered in Basel, Switzerland.
The company’s Selector software solution combines genomics from next-generation sequencing (NGS) and other omics technologies, such that “cell lines can be engineered for specific purposes, such as optimizing glycosylation profiles or other relevant parameters,” Johnson explains. “Essentially, this approach allows scientists to transition from a trial-and-error process to a rational cell line development approach.”
This approach begins with a comprehensive understanding of genomics specific to the host cell line, such as Chinese hamster ovary (CHO) cells, that will be used for protein production. By integrating genomic information along with other omics technologies—including proteomics, metabolomics, and methylation data—cell line development scientists can produce a more holistic view of the factors underlying important cell culture processes.
“This approach can reveal relevant pathways and potential bottlenecks impacting biotherapeutic production, which would not be identified using a single method,” says Johnson. The results from Genedata’s analysis include a list of “genes that could be engineered to optimize production of a specific therapeutic modality—for instance, for a specific antibody format,” Johnson notes.
Using this information, companies can improve bioprocessing in various ways, starting with the fundamentals.
“By leveraging multi-omics data, scientists can derive deeper insights into the underlying cell biology and identify actionable targets for process optimization,” Johnson says. “This approach requires an integrated data-management, processing and analysis platform that enables users to visualize and integrate complex transcriptional and metabolic processes.” With that information, Johnson concludes: “NGS-based cell line development enables faster timelines and greater control over scale-up and commercial manufacturing.”
