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The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies
The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target‐mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196441/ https://www.ncbi.nlm.nih.gov/pubmed/36752286 http://dx.doi.org/10.1002/psp4.12927 |
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author | Pressly, Michelle A. Peletier, Lambertus A. Zheng, Songmao Sharma, Vishnu D. Lien, Yi Ting (Kayla) Wang, Weirong Zhou, Honghui Schmidt, Stephan |
author_facet | Pressly, Michelle A. Peletier, Lambertus A. Zheng, Songmao Sharma, Vishnu D. Lien, Yi Ting (Kayla) Wang, Weirong Zhou, Honghui Schmidt, Stephan |
author_sort | Pressly, Michelle A. |
collection | PubMed |
description | The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target‐mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well‐established, physiologically based model by Li et al. in a step‐wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self‐guided exploration and hands‐on analysis. |
format | Online Article Text |
id | pubmed-10196441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101964412023-05-20 The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies Pressly, Michelle A. Peletier, Lambertus A. Zheng, Songmao Sharma, Vishnu D. Lien, Yi Ting (Kayla) Wang, Weirong Zhou, Honghui Schmidt, Stephan CPT Pharmacometrics Syst Pharmacol Tutorials The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target‐mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well‐established, physiologically based model by Li et al. in a step‐wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self‐guided exploration and hands‐on analysis. John Wiley and Sons Inc. 2023-03-23 /pmc/articles/PMC10196441/ /pubmed/36752286 http://dx.doi.org/10.1002/psp4.12927 Text en © 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Tutorials Pressly, Michelle A. Peletier, Lambertus A. Zheng, Songmao Sharma, Vishnu D. Lien, Yi Ting (Kayla) Wang, Weirong Zhou, Honghui Schmidt, Stephan The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies |
title | The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies |
title_full | The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies |
title_fullStr | The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies |
title_full_unstemmed | The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies |
title_short | The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies |
title_sort | quest for balance between capturing data and model complexity: a quantitative clinical pharmacology approach applied to monoclonal antibodies |
topic | Tutorials |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196441/ https://www.ncbi.nlm.nih.gov/pubmed/36752286 http://dx.doi.org/10.1002/psp4.12927 |
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