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Integrated Process Modeling—A Process Validation Life Cycle Companion

During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of si...

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Autores principales: Zahel, Thomas, Hauer, Stefan, Mueller, Eric M., Murphy, Patrick, Abad, Sandra, Vasilieva, Elena, Maurer, Daniel, 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/PMC5746753/
https://www.ncbi.nlm.nih.gov/pubmed/29039771
http://dx.doi.org/10.3390/bioengineering4040086
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author Zahel, Thomas
Hauer, Stefan
Mueller, Eric M.
Murphy, Patrick
Abad, Sandra
Vasilieva, Elena
Maurer, Daniel
Brocard, Cécile
Reinisch, Daniela
Sagmeister, Patrick
Herwig, Christoph
author_facet Zahel, Thomas
Hauer, Stefan
Mueller, Eric M.
Murphy, Patrick
Abad, Sandra
Vasilieva, Elena
Maurer, Daniel
Brocard, Cécile
Reinisch, Daniela
Sagmeister, Patrick
Herwig, Christoph
author_sort Zahel, Thomas
collection PubMed
description During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events.
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spelling pubmed-57467532018-01-03 Integrated Process Modeling—A Process Validation Life Cycle Companion Zahel, Thomas Hauer, Stefan Mueller, Eric M. Murphy, Patrick Abad, Sandra Vasilieva, Elena Maurer, Daniel Brocard, Cécile Reinisch, Daniela Sagmeister, Patrick Herwig, Christoph Bioengineering (Basel) Article During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events. MDPI 2017-10-17 /pmc/articles/PMC5746753/ /pubmed/29039771 http://dx.doi.org/10.3390/bioengineering4040086 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
Hauer, Stefan
Mueller, Eric M.
Murphy, Patrick
Abad, Sandra
Vasilieva, Elena
Maurer, Daniel
Brocard, Cécile
Reinisch, Daniela
Sagmeister, Patrick
Herwig, Christoph
Integrated Process Modeling—A Process Validation Life Cycle Companion
title Integrated Process Modeling—A Process Validation Life Cycle Companion
title_full Integrated Process Modeling—A Process Validation Life Cycle Companion
title_fullStr Integrated Process Modeling—A Process Validation Life Cycle Companion
title_full_unstemmed Integrated Process Modeling—A Process Validation Life Cycle Companion
title_short Integrated Process Modeling—A Process Validation Life Cycle Companion
title_sort integrated process modeling—a process validation life cycle companion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5746753/
https://www.ncbi.nlm.nih.gov/pubmed/29039771
http://dx.doi.org/10.3390/bioengineering4040086
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