In practice, things often go wrong, even with a well-planned design of experiments (DOE). Operators might make errors, or instruments might not cooperate. In biologics, experiments can be expensive, even on a small scale. What happens if something goes wrong in the execution of an already costly experiment? Is it possible to rescue the DOE as soon as it deviates from the plan without wasting any completed runs? We share two examples using the JMP DOE platform to plan, evaluate, and modify designs in biologics.
In Example 1, we planned an I-optimal design with five continuous factors and one blocking factor representing two instruments. During execution, the operator deviated from the planned instrument ID and realized the mistake after completing one-third of the runs. To rescue the design, we reassigned the block IDs for the remaining runs to the two instruments in an order that achieved comparable design properties to the original.
In Example 2, we planned an A-optimal split-plot design, but the scientists realized that the instrument could not tolerate the high pressure settings at the start of the second whole-plot runs. Due to resource limitations, we prioritized saving the completed runs. Since it is not straightforward to augment a split-plot design, we moved forward with a modified design that incorporated the completed runs, resulting in less desirable design properties, as acknowledged by the team.
These examples illustrate how we can adapt and rescue DOEs in response to challenges, emphasizing the importance of flexibility in experimental design.
