Important Aspects of the Design of Experiments and Data Treatment in the Analytical Quality by Design Framework for Chromatographic Method Development

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Abstract
In the analytical quality by design (AQbD) framework, the design of experiments (DOE) plays a very important role, as it provides information about how experimental input variables influence critical method attributes. Based on the information obtained from the DOE, mathematical models are generated and used to build the method operable design region (MODR), which is a robust region of operability. Data treatment steps are usually carried out in software such as Fusion QbD, Minitab, or StaEase 360, among others. Although there are many studies in the literature that use the DOE, none of them address important aspects of data treatment for optimization and MODR generation and compare different software calculations. The purpose of this study is to contribute to a better understanding of data treatment aspects that are frequently misread or not fully understood, such as model selection, ANOVA results, and residual analysis. The discussion will be guided by the separation of curcuminoids, using ultra-high performance liquid chromatography and eight quality attributes as responses. This study highlights the importance of carefully selecting and evaluating models, as they significantly influence the generation of the MODR. Moreover, the findings emphasize that it is essential to incorporate uncertainties into the contour plots to accurately determine the MODR in compliance with the ICH Q14 guidelines and USP General Chapter .
Funding Information
  • B.F.G.P Ph.D. Scholarship (001)
  • CNPq/FAPESP (001)

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