ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Equivalence Approach for Multi-Factor Robustness Evaluation with Application in Vaccines Development

4 September 2018, 10:50 – 11:10


Submitted by
Waldemar Miller
Bernard Francq (GSK, CMC StatS), Dan Lin (GSK, CMC StatS), Waldemar Miller (GSK, CMC StatS, Universität Magdeburg), Réjane Rousseau (GSK, CMC StatS), Sylvie Scolas (GSK, CMC StatS), Walter Hoyer (GSK, CMC StatS)
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 into “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 [1] or its extension by Wiens and Iglewicz [2]), a “DOE for flatness” extends the equivalence test to the multi-dimensional case (continuous or categorical factors, e.g. temperature or duration). We discuss adaptation of the significance level and tackling the multiplicity issue as the entire experimental domain is compared to a given reference level by contrasts of mean responses between every point and the reference. 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.

[1] Schuirmann D.J. A comparison of the two one-sided tests procedure and the power approach for assessing equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15, 657–680, 1987
[2] Wiens B.L., Iglewicz B. On testing equivalence of three populations. Journal of Biopharmaceutical Statistics, 9, 465–483, 1999

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