Next Article in Journal
Profiling the Fatty Acids Content of Ornamental Camellia Seeds Cultivated in Galicia by an Optimized Matrix Solid-Phase Dispersion Extraction
Next Article in Special Issue
Integrated Process Modeling—A Process Validation Life Cycle Companion
Previous Article in Journal
The Effects of Alkyl Chain Combinations on the Structural and Mechanical Properties of Biomimetic Ion Pair Amphiphile Bilayers
Previous Article in Special Issue
Estimating Extrinsic Dyes for Fluorometric Online Monitoring of Antibody Aggregation in CHO Fed-Batch Cultivations
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessArticle
Bioengineering 2017, 4(4), 85; https://doi.org/10.3390/bioengineering4040085

Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1

1
Exputec GmbH, Mariahilferstraße 147, 1150 Vienna, Austria
2
Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
3
Versartis Inc., 4200 Bohannon Drive, Suite 250, Menlo Park, CA 94025, USA
4
Software Competence Center Hagenberg, Softwarepark 21, 4232 Hagenberg, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Daniel G. Bracewell
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)
Full-Text   |   PDF [1491 KB, uploaded 12 October 2017]   |  

Abstract

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. View Full-Text
Keywords: retrospective power analysis; process characterization study; process validation stage 1; criticality assessment; control strategy; design of experiments retrospective power analysis; process characterization study; process validation stage 1; criticality assessment; control strategy; design of experiments
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

Zahel, T.; Marschall, L.; Abad, S.; Vasilieva, E.; Maurer, D.; Mueller, E.M.; Murphy, P.; Natschläger, T.; Brocard, C.; Reinisch, D.; Sagmeister, P.; Herwig, C. Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1. Bioengineering 2017, 4, 85.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Bioengineering EISSN 2306-5354 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top