A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.
Model development
Model identifiability
Quantitative systems pharmacology
Journal
Bulletin of mathematical biology
ISSN: 1522-9602
Titre abrégé: Bull Math Biol
Pays: United States
ID NLM: 0401404
Informations de publication
Date de publication:
07 02 2022
07 02 2022
Historique:
received:
19
01
2021
accepted:
30
11
2021
entrez:
8
2
2022
pubmed:
9
2
2022
medline:
3
5
2022
Statut:
epublish
Résumé
There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this, there is no "gold standard" for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modeling communities, both the QSP community and related disciplines. This perspective article highlights the pros and cons of using simple (often identifiable) vs. complex (more physiologically detailed but often non-identifiable) models, as well as aspects of parameter identifiability, sensitivity and inference methodologies for model development and analysis. The paper distills the central themes of the issue of identifiability and optimal model size and discusses open challenges.
Identifiants
pubmed: 35132487
doi: 10.1007/s11538-021-00982-5
pii: 10.1007/s11538-021-00982-5
pmc: PMC8821410
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
39Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 212203/Z/18/Z
Pays : United Kingdom
Informations de copyright
© 2022. The Author(s).
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