Innovation in Pharmaceutical Experimentation Part 1: Review of Experimental Designs Used in Industrial Pharmaceutics Research and Introduction to Bayesian D-Optimal Experimental Design

The concept of design space, as described in the ICH Harmonized Tripartite Guideline Q8 for Pharmaceutical Development (Q8 Pharmaceutical development, ICH harmonized tripartite guidelines, in International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use, 2005), was introduced to justify regulatory flexibility in pharmaceutical manufacturing operations. The basis for this concept is that advanced understanding of variables affecting product quality, obtained either through historical operation or demonstrated through process modeling, justifies replacement of traditional process targets with acceptable operational ranges. Process adjustments that allow operation within the region defined by the design space do not require regulatory oversight. Whereas there are many advantages to having the flexibility to operate within such ranges, the concept is only valid when the design space has been adequately described by appropriate experimentation. Given the complexity of pharmaceutical processes and the number of variables to consider in developing operational ranges, only a well-executed program of experiments, supported by appropriate statistical analyses, could provide the necessary information to truly capture a design space. Thus, the risk of employing a design space is that the model will be applied to a region outside of the approved design space, either through a false statistical inference or by omitting some important factor effects. This article presents a review of the experimental strategies typically employed to develop pharmaceutical processes with special emphasis on the assumptions and limitations of the approaches. An alternative strategy that provides an opportunity to build on previous information efficiently without requiring extraordinary skill in statistics, Bayesian optimal design, is introduced as an alternative to the classical approaches.

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