Cell Culture Media Optimization

Media optimization can be important for a number of reasons in cell culture applications, dependent on the product and associated priorities. 

Cell lines used to produce biologics prioritize product titer and quality over other metrics due to the relatively low contribution of media to overall costs. In this scenario, media composition is constantly optimized and re-optimized alongside process development. This ensures that the media meets the underlying needs of the process in development to continually support high product quality.

Mammalian cell lines can also be used to produce products with more of a focus on economics. In this scenario, media costs may be a factor of importance, and consequently media development may encompass optimizing both for maximizing performance and decreasing cost.

Complexity of formulation

Media composition can be challenging due to the complexity of preparation. In order to generate a representative and consistent medium, formulation is of utmost importance. Suboptimal formulation due to factors such as different components or chemical impurities can result in differences in cell line performance. Once a process reaches commercial operations, lot to lot differences in raw materials can impact cell line performance. 

Cell line differences

There is a risk when developing cell lines that media composition may impact different cell lines differently. Therefore, studies should be performed to ensure that lessons apply broadly and not just to a single clone.

Cell culture media studies work similarly to any other studies at Culture, where customers specify the conditions, work with the Culture team to finalize experimental details, and then the experiment is executed with real-time data visualization. 

Culture offers the capacity required to perform studies in order to optimize media composition. In our Cloud Lab, capacity is available to perform larger studies (such as DOEs) to identify the main factors that influence performance, as well as potential interactions between variables in order to rapidly identify optimal media conditions. Different studies can also be executed in parallel, allowing multiple studies to be performed simultaneously, accelerating project timelines.