Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
Exputec GmbH, Mariahilferstraße 147, 1150 Vienna, Austria
Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
Versartis Inc., 4200 Bohannon Drive, Suite 250, Menlo Park, CA 94025, USA
Software Competence Center Hagenberg, Softwarepark 21, 4232 Hagenberg, Austria
Author to whom correspondence should be addressed.
Academic Editor: Daniel G. Bracewell
Bioengineering 2017, 4(4), 85; https://doi.org/10.3390/bioengineering4040085
Received: 7 September 2017 / Revised: 5 October 2017 / Accepted: 7 October 2017 / Published: 12 October 2017
(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)
Identification of critical process parameters that impact product quality is a central task during regulatory requested process validation. Commonly, this is done via design of experiments and identification of parameters significantly impacting product quality (rejection of the null hypothesis that the effect equals 0). However, parameters which show a large uncertainty and might result in an undesirable product quality limit critical to the product, may be missed. This might occur during the evaluation of experiments since residual/un-modelled variance in the experiments is larger than expected a priori. Estimation of such a risk is the task of the presented novel retrospective power analysis permutation test. This is evaluated using a data set for two unit operations established during characterization of a biopharmaceutical process in industry. The results show that, for one unit operation, the observed variance in the experiments is much larger than expected a priori, resulting in low power levels for all non-significant parameters. Moreover, we present a workflow of how to mitigate the risk associated with overlooked parameter effects. This enables a statistically sound identification of critical process parameters. The developed workflow will substantially support industry in delivering constant product quality, reduce process variance and increase patient safety.