Once a high-producing, stable cell line is available, the respective bioprocess needs to be optimized to further increase product yields and quality. The exploratory processes applied during cell-line development must be transferred to manufacturing scales.
Moreover, cell lines must be adapted to suspension cultures and to serum-free (e.g. animal-component free) or chemically defined, optimized media (CDACF, SFM, PFM). These are challenging activities that have a profound effect on productivity.
Typically, the optimization process defines a fed-batch process that is based on a basal medium supporting initial growth and production, and a growth medium providing optimized supplementation of nutrients. In addition, process parameter settings such as feeding strategy, temperature, pO2, pH must be systematically evaluated.
During the culture adaptation of the cells, metabolic parameters (e.g., consumption rate, lactose), cell growth and viability, as well as product titers are monitored for different production volumes, mixing times, O2 transfer, agitation rates, cell densities and others.
Given the large sets of variables in a process optimization campaign, there is a strong need for an integrative data-management solution to track and analyze all relevant data (Figure 3). An integrated biologics platform provides flexible storage for all bioprocess development data including assay and analytics results (e.g. ELISA, Biacore, FACS, SDS-PAGE, SEC, DLS, MS).
Powerful visualization and analysis functionalities enable researchers to closely monitor the optimization process and to select best process parameters—all of which help to direct and guide the optimization process in a structured and organized mode, which saves valuable resources and time.
A scalable workflow, data management, and analysis platform for tracking, sharing and analyzing all data is required to effectively cope with and harness information from the volume of data and complexity of today’s bioprocess development activities. This system, which eliminates potential errors during DNA synthesis, construct design and subsequent protein production, has resulted in significant cost savings.
By supporting timely capture and evaluation of cell-line development data, it is possible to employ full automation, expand panels of cell-line evaluations, and consider earlier cell-line development work already during the research phase.
A data-driven approach during media optimization and upscaling production minimized costs by facilitating timely, strategic decision-making and guiding process development.