Design of Experiments (DoE) facilitates the screening for relevant factors and the optimization of the latter in terms of one or several responses. The number of factors that can be included in screening designs is substantial, but may still be a bottleneck if complex processes with interdependent steps have to be optimized. For example, cryopreservation protocols for plant cells consist of more than 5 steps, each of which can depend on more than 10 factors. The way in which the steps affect each other can be complex (i.e. effects may only appear in the second next step or so) and difficult to predict. However, improving such protocols is important in the context of biopharmaceutical manufacturing because they affect the quality of cryo-stocks necessary to ensure a consistent batch-to-batch quality of the biological starting material as well as the product.
Here we present an iterative approach to structure the multi-parameter problem and illustrate how challenges regarding the experimental implementation of a design can be handled. We highlight how the resulting models can support a quality by design approach as recommended by the regulatory authorities for biopharmaceutical manufacturing. Therefore, we think that our data will be of interest to colleagues working in this research area as well as for all those developing complex procedures that require optimized working conditions and detailed documentation.
June 18 at 9:00am Central Time