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Evaluation framework for systems models

As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper...

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Detalles Bibliográficos
Autores principales: Braakman, Sietse, Pathmanathan, Pras, Moore, Helen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923730/
https://www.ncbi.nlm.nih.gov/pubmed/34921743
http://dx.doi.org/10.1002/psp4.12755
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author Braakman, Sietse
Pathmanathan, Pras
Moore, Helen
author_facet Braakman, Sietse
Pathmanathan, Pras
Moore, Helen
author_sort Braakman, Sietse
collection PubMed
description As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used.
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spelling pubmed-89237302022-03-21 Evaluation framework for systems models Braakman, Sietse Pathmanathan, Pras Moore, Helen CPT Pharmacometrics Syst Pharmacol White Papers As decisions in drug development increasingly rely on predictions from mechanistic systems models, assessing the predictive capability of such models is becoming more important. Several frameworks for the development of quantitative systems pharmacology (QSP) models have been proposed. In this paper, we add to this body of work with a framework that focuses on the appropriate use of qualitative and quantitative model evaluation methods. We provide details and references for those wishing to apply these methods, which include sensitivity and identifiability analyses, as well as concepts such as validation and uncertainty quantification. Many of these methods have been used successfully in other fields, but are not as common in QSP modeling. We illustrate how to apply these methods to evaluate QSP models, and propose methods to use in two case studies. We also share examples of misleading results when inappropriate analyses are used. John Wiley and Sons Inc. 2022-01-10 2022-03 /pmc/articles/PMC8923730/ /pubmed/34921743 http://dx.doi.org/10.1002/psp4.12755 Text en © 2021 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the 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 White Papers
Braakman, Sietse
Pathmanathan, Pras
Moore, Helen
Evaluation framework for systems models
title Evaluation framework for systems models
title_full Evaluation framework for systems models
title_fullStr Evaluation framework for systems models
title_full_unstemmed Evaluation framework for systems models
title_short Evaluation framework for systems models
title_sort evaluation framework for systems models
topic White Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923730/
https://www.ncbi.nlm.nih.gov/pubmed/34921743
http://dx.doi.org/10.1002/psp4.12755
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