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Holistic Design of Experiments Using an Integrated Process Model

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, the...

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Autores principales: Oberleitner, Thomas, Zahel, Thomas, Pretzner, Barbara, Herwig, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687958/
https://www.ncbi.nlm.nih.gov/pubmed/36354553
http://dx.doi.org/10.3390/bioengineering9110643
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author Oberleitner, Thomas
Zahel, Thomas
Pretzner, Barbara
Herwig, Christoph
author_facet Oberleitner, Thomas
Zahel, Thomas
Pretzner, Barbara
Herwig, Christoph
author_sort Oberleitner, Thomas
collection PubMed
description Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality.
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spelling pubmed-96879582022-11-25 Holistic Design of Experiments Using an Integrated Process Model Oberleitner, Thomas Zahel, Thomas Pretzner, Barbara Herwig, Christoph Bioengineering (Basel) Article Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality. MDPI 2022-11-03 /pmc/articles/PMC9687958/ /pubmed/36354553 http://dx.doi.org/10.3390/bioengineering9110643 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Oberleitner, Thomas
Zahel, Thomas
Pretzner, Barbara
Herwig, Christoph
Holistic Design of Experiments Using an Integrated Process Model
title Holistic Design of Experiments Using an Integrated Process Model
title_full Holistic Design of Experiments Using an Integrated Process Model
title_fullStr Holistic Design of Experiments Using an Integrated Process Model
title_full_unstemmed Holistic Design of Experiments Using an Integrated Process Model
title_short Holistic Design of Experiments Using an Integrated Process Model
title_sort holistic design of experiments using an integrated process model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687958/
https://www.ncbi.nlm.nih.gov/pubmed/36354553
http://dx.doi.org/10.3390/bioengineering9110643
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