Design Spaces for analytical methods

Since the adoption of the ICH Q8 document concerning the development of pharmaceutical processes following a Quality by Design (QbD) approach, there have been many discussions on the opportunity for analytical method developments to follow a similar approach. A key component of the QbD paradigm is the definition of the Design Space (DS) of analytical methods where assurance of quality is provided. Several DSs for analytical methods have been published, stressing the importance of this concept.This article aims to explain what an analytical method DS is, why it is useful for the robust development and optimization of analytical methods and how to build such a DS. We distinguish the usual mean response surface approach, overlapping mean response surfaces and the desirability function as only they correctly define a DS. We also review and discuss recent publications assessing the DS of analytical methods.

► Design of Experiments is useful to define Design Spaces for analytical methods.
► Mean response surfaces are not sufficient for Design Spaces for analytical methods.
► Design Spaces for analytical methods require probability evaluation.
► Bayesian statistics allows correct definition of Design Spaces for analytical methods.
► Critical review of published Design Spaces for analytical methods.