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Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1

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...

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Autores principales: Zahel, Thomas, Marschall, Lukas, Abad, Sandra, Vasilieva, Elena, Maurer, Daniel, Mueller, Eric M., Murphy, Patrick, Natschläger, Thomas, Brocard, Cécile, Reinisch, Daniela, Sagmeister, Patrick, Herwig, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746752/
https://www.ncbi.nlm.nih.gov/pubmed/29023375
http://dx.doi.org/10.3390/bioengineering4040085
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author Zahel, Thomas
Marschall, Lukas
Abad, Sandra
Vasilieva, Elena
Maurer, Daniel
Mueller, Eric M.
Murphy, Patrick
Natschläger, Thomas
Brocard, Cécile
Reinisch, Daniela
Sagmeister, Patrick
Herwig, Christoph
author_facet Zahel, Thomas
Marschall, Lukas
Abad, Sandra
Vasilieva, Elena
Maurer, Daniel
Mueller, Eric M.
Murphy, Patrick
Natschläger, Thomas
Brocard, Cécile
Reinisch, Daniela
Sagmeister, Patrick
Herwig, Christoph
author_sort Zahel, Thomas
collection PubMed
description 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.
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spelling pubmed-57467522018-01-03 Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1 Zahel, Thomas Marschall, Lukas Abad, Sandra Vasilieva, Elena Maurer, Daniel Mueller, Eric M. Murphy, Patrick Natschläger, Thomas Brocard, Cécile Reinisch, Daniela Sagmeister, Patrick Herwig, Christoph Bioengineering (Basel) Article 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. MDPI 2017-10-12 /pmc/articles/PMC5746752/ /pubmed/29023375 http://dx.doi.org/10.3390/bioengineering4040085 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zahel, Thomas
Marschall, Lukas
Abad, Sandra
Vasilieva, Elena
Maurer, Daniel
Mueller, Eric M.
Murphy, Patrick
Natschläger, Thomas
Brocard, Cécile
Reinisch, Daniela
Sagmeister, Patrick
Herwig, Christoph
Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
title Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
title_full Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
title_fullStr Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
title_full_unstemmed Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
title_short Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1
title_sort workflow for criticality assessment applied in biopharmaceutical process validation stage 1
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746752/
https://www.ncbi.nlm.nih.gov/pubmed/29023375
http://dx.doi.org/10.3390/bioengineering4040085
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