During the development of biopharmaceuticals, various studies are conducted, and one of the most frequently used research techniques is DOE. Using DOE, specific responses are modeled from various factors, important factors are identified, and optimal process operation methods are derived/verified. When performing DOE to develop a biopharmaceutical process, various curve data can be confirmed. The results are not always expressed as specific numbers, but rather, the results are often provided as curve data at various points in time. Results include cell growth curves in the culture process, productivity, chromatographs in purification, stability patterns, and various curves in analysis. In this case, it is easy to perform DOE analysis by taking the values at a specific point in time as responses or interpreting the results within a limited model. However, if you use JMP Pro's Curved DOE or Functional DOE, you can interpret curve data as responses, not responses at a specific point in time. In this presentation, I would like to introduce a case of modeling curve data with DOE for biopharmaceutical development.
