The Expected Design Space for analytical methods: a new perspective based on modeling and prediction

The Design Space (DS) of an analytical method is defined as the set of factor settings that provides satisfactory results, with respect to pre-defined constraints. The proposed methodology aims at identifying a region in the space of factors that will likely provide satisfactory results during the future use of the analytical method in routine, through an optimization process of this method.

First, the DS is statistically defined as derived from the β-Expectation prediction interval. Second, multi-criteria perspective is added in this definition as it is often required for optimizing analytical method. Finally, a Monte-Carlo simulation is envisaged to numerically predict and identify the DS under uncertainty.

Examples based on high-performance liquid chromatography (HPLC) methods will be given, illustrating the applicability of the methodology.