ABSTRACT
Optimizing a formulation buffer for biological products such as vaccines or monoclonal antibodies is a complex task. One of the reasons for this is that the number of formulation ingredients make it difficult to cost efficiently design experiments. The FDA’s Process Validation (PV) initiative insists on continuously improving the understanding of manufacturing processes, together with accurately estimating their robustness and reliability. During design and characterization studies, they suggest the derivation of a design space, a concept introduced by the ICH Q8 as “the multidimensional combination and interaction of input variables (e.g., materials attributes) and process parameters that have been demonstrated to provide assurance of quality.”
Deriving a design space to optimize multiple quality attributes isn’t easy, and the uncertainty of the estimation is often ignored by scientists and statisticians. Using Bayesian methods to predict future results, and modern design of experiment (DoE) to efficiently explore the complete experimental design, this chapter presents how the quality by design paradigm can be applied to pharmaceutical formulation.
