(593a) Improving Process Understanding through Good Experimental Planning

The pharmaceutical industries ability to have manufacturing flexibility given they can demonstrate proper process understanding has brought a resurgence in the application of statistically designed experiments. Even though Design of Experiments (DoE) has been used extensively across all industries to characterize and optimize a manufacturing process, it is all too often their application falls short of its objective. Given the large emphasis to define and prove the manufacturing design space, it is imperative that the pharmaceutical industry use a planned and systematic approach to study the relationship between process parameters affecting those quality attributes critical to patient safety and efficacy. The selection of the proper experimental design requires correctly identifying the problem, associated factor ranges, and accompanying constraints. A large amount of literature exists on the theory and application of DoE, however minimal attention is given to the planning and sequential approach required to achieve the desired outcome. The focus of this paper is to provide guidelines for appropriate experimental planning, and a detailed summary of existing DoE and how their sequential application can support the development of the products design space and an efficient biopharmaceutical process.