As companies use models to an increasing extent for design space development, there is rightly an increased focus on model statistics and model parameter uncertainty.
John Peterson and colleagues from the statistics group at GSK have published a useful article on how to factor uncertainty into a DOE-based design space with multiple responses, consistent with ICH Q8. This paper, just published, is available at http://dx.doi.org/10.1080/10543400802278197 and highlights limitations of the commonly used procedure of overlapping average responses to define a design space.
The DynoChem team has been looking at much the same issue with first principles / mechanistic models, i.e. how to carve out a design space based on the probability of successful operation there. A posting with example calculations will be made here shortly.
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