The ICH Q8 Definition of Design Space: A Comparison of the Overlapping Means and the Bayesian Predictive Approaches

The ICH Q8 defines “design space” (DS) as “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” Unfortunately, some pharmaceutical scientists appear to misinterpret the definition of DS as a process monitoring strategy. A more subtle and possibly more misleading issue, however, is the application of standard response surface methodology software applications in an attempt to construct a DS. The methodology of “overlapping mean responses” (OMR), available in many point-and-click oriented statistical packages, provides a tempting opportunity to use this methodology to create a DS. Furthermore, a few recent (and two possibly very influential) papers have been published that appear to propose the use of OMR as a way to construct a DS. However, such a DS may harbor operating conditions with a low probability of meeting process specifications. In this article we compare the OMR approach with a Bayesian predictive approach to DS, and show that the OMR approach produces DS’s that are too large and may contain conditions with a low probability of meeting process specifications. In some cases, even the best operating conditions do not have a high probability of meeting all process specifications.