using statistical DoE during the PD stage is a proven means to establish process/ product knowledge. DoE-based approaches can provide a deeper understanding of how product quality attributes and process-related impurities change when several process operating parameters vary simultaneously for a given unit operation, and thus lead to increased process knowledge with respect to the robustness of a culture step or combination of culture steps. Prior to designing a DoE study, a risk assess­ment is typically conducted to rank the risk of process parameters regarding the impact on process performance and product quality. This is based on the previous knowledge from the platform process, rst principles, early development data, and literature review, etc. Based on the outcome of risk assessment, follow-up studies will be executed to identify the acceptable range for process parameters [3]. Since most bioprocess unit steps easily have more than four factors, the rst round of experiments typically uses the screening design (e.g., Plackett-Burman method) to identify the most important factors early in the experimentation phase when com­plete knowledge about the system is usually unavailable [4]. Once the important factors are identied, a follow-up DoE study (e.g., response surface designs or fac­torial design) to understand the interaction, and to rene the model. Note: Response surface design is recommended especially if there is suspected curvature in the response surface. Once the model is rened, the model could be used to predict worst-case conditions for certain process output, and the predicted worst-case con­ditions could be further tested in experiments. Separately, certain univariate studies could be performed to provide knowledge for ongoing manufacturing support. For example, a pH excursion study (effect of the short-term high/low pH) is typically executed to understand the effect of the pH probe spike issue occasionally observed in commercial manufacturing.