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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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. |
format | Online Article Text |
id | pubmed-5746753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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