Bernard Francq and Sylvie Scolas, GlaxoSmithKline

Current state-of-the-art vaccines development is based on the “Quality-by-Design” paradigm, where risk-based and data driven decisions are key. A prominent example is the classification of process parameters as “critical” and “non-critical” based on a series of Designs of Experiments (DoE) performed during vaccine development. This helps to understand the relationship between “Critical Process Parameters” (CPPs) and “Critical Quality Attributes” (CQAs) and then to establish the “Design space”. Design spaces are defined according to the ICH guidance Q8 as a subspace of process parameter combinations “that have been demonstrated to provide assurance of quality.”

In that context, the robustness of a process is its property to stay within the specification limits (target ± Δ) after a change in experimental conditions. In analogy to the classical equivalence test (see e.g.  Schuirmann’s Two One-Sided Test [TOST] procedure), a “DOE for flatness” extends the equivalence test to the multi-dimensional case (continuous or categorical factors, e.g. temperature or a duration). We discuss adaptation of the significance level and tackling the multiplicity issue for the entire experimental domain by using contrasts of mean responses between every experimental point and the reference point (e.g., the standard experimental condition). A comparison between the univariate (unadjusted contrast) and multivariate t-distribution (for adjustment) will be performed. The design space is then the subset of the multi-dimensional space where the predicted means are equivalent to the reference level, i.e., confidence intervals of mean contrasts lie within ± Δ.

Performance of our methodology will be evaluated by means of simulations and applications to case studies within CMC statistics and vaccines development.