About Model Validation in Bioprocessing
In bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guidelines on how models for QbD are validated are still missing. In this review we provide a comprehensive overview of the validation methods, mathematical approache...
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doaj-03cfd18e975f436f8f34114645a0ee9f2021-06-01T01:30:28ZengMDPI AGProcesses2227-97172021-05-01996196110.3390/pr9060961About Model Validation in BioprocessingVignesh Rajamanickam0Heiko Babel1Liliana Montano-Herrera2Alireza Ehsani3Fabian Stiefel4Stefan Haider5Beate Presser6Bettina Knapp7Boehringer Ingelheim RCV GmbH & Co KG1, Biopharmaceuticals Austria, Dr. Boehringer Gasse 5-11, A-1121 Vienna, AustriaBoehringer Ingelheim Pharma GmbH & Co.KG, Biopharmaceuticals Germany, Birkendorfer Strasse 65, D-88397 Biberach an der Riß, GermanyBoehringer Ingelheim Pharma GmbH & Co.KG, Development Biologicals Germany, Birkendorfer Strasse 65, D-88397 Biberach an der Riß, GermanyBoehringer Ingelheim Pharma GmbH & Co.KG, Development Biologicals Germany, Birkendorfer Strasse 65, D-88397 Biberach an der Riß, GermanyBoehringer Ingelheim Pharma GmbH & Co.KG, Development Biologicals Germany, Birkendorfer Strasse 65, D-88397 Biberach an der Riß, GermanyBoehringer Ingelheim RCV GmbH & Co KG1, Biopharmaceuticals Austria, Dr. Boehringer Gasse 5-11, A-1121 Vienna, AustriaBoehringer Ingelheim Pharma GmbH & Co.KG, Development Biologicals Germany, Birkendorfer Strasse 65, D-88397 Biberach an der Riß, GermanyBoehringer Ingelheim Pharma GmbH & Co.KG, Development Biologicals Germany, Birkendorfer Strasse 65, D-88397 Biberach an der Riß, GermanyIn bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guidelines on how models for QbD are validated are still missing. In this review we provide a comprehensive overview of the validation methods, mathematical approaches, and metrics currently applied in bioprocess modeling. The methods cover analytics for data used for modeling, model training and selection, measures for predictiveness, and model uncertainties. We point out the general issues in model validation and calibration for different types of models and put this into the context of existing health authority recommendations. This review provides a starting point for developing a guide for model validation approaches. There is no one-fits-all approach, but this review should help to identify the best fitting validation method, or combination of methods, for the specific task and the type of bioprocess model that is being developed.https://www.mdpi.com/2227-9717/9/6/961bioprocess modelsmodel validationmodel calibrationquality by designmechanistical and statistical modelshybrid models |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vignesh Rajamanickam Heiko Babel Liliana Montano-Herrera Alireza Ehsani Fabian Stiefel Stefan Haider Beate Presser Bettina Knapp |
spellingShingle |
Vignesh Rajamanickam Heiko Babel Liliana Montano-Herrera Alireza Ehsani Fabian Stiefel Stefan Haider Beate Presser Bettina Knapp About Model Validation in Bioprocessing Processes bioprocess models model validation model calibration quality by design mechanistical and statistical models hybrid models |
author_facet |
Vignesh Rajamanickam Heiko Babel Liliana Montano-Herrera Alireza Ehsani Fabian Stiefel Stefan Haider Beate Presser Bettina Knapp |
author_sort |
Vignesh Rajamanickam |
title |
About Model Validation in Bioprocessing |
title_short |
About Model Validation in Bioprocessing |
title_full |
About Model Validation in Bioprocessing |
title_fullStr |
About Model Validation in Bioprocessing |
title_full_unstemmed |
About Model Validation in Bioprocessing |
title_sort |
about model validation in bioprocessing |
publisher |
MDPI AG |
series |
Processes |
issn |
2227-9717 |
publishDate |
2021-05-01 |
description |
In bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guidelines on how models for QbD are validated are still missing. In this review we provide a comprehensive overview of the validation methods, mathematical approaches, and metrics currently applied in bioprocess modeling. The methods cover analytics for data used for modeling, model training and selection, measures for predictiveness, and model uncertainties. We point out the general issues in model validation and calibration for different types of models and put this into the context of existing health authority recommendations. This review provides a starting point for developing a guide for model validation approaches. There is no one-fits-all approach, but this review should help to identify the best fitting validation method, or combination of methods, for the specific task and the type of bioprocess model that is being developed. |
topic |
bioprocess models model validation model calibration quality by design mechanistical and statistical models hybrid models |
url |
https://www.mdpi.com/2227-9717/9/6/961 |
work_keys_str_mv |
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