A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability

Sher, Anna; Niederer, Steven A.; Mirams, Gary R.; Kirpichnikova, Anna; Allen, Richard; Pathmanathan, Pras; Gavaghan, David J.; van der Graaf, Piet H.; Noble, Denis

Abstract

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.

Citation

Sher, A., Niederer, S. A., Mirams, G. R., Kirpichnikova, A., Allen, R., Pathmanathan, P., …Noble, D. (2022). A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability. Bulletin of Mathematical Biology, 84(3), Article 39. https://doi.org/10.1007/s11538-021-00982-5

• Journal Article Type: Article
• Acceptance Date: Nov 30, 2021
• Online Publication Date: Feb 7, 2022
• Publication Date: 2022-03
• Deposit Date: Jan 11, 2022
• Publicly Available Date: Feb 8, 2023
• Journal: Bulletin of Mathematical Biology
• Print ISSN: 0092-8240
• Electronic ISSN: 1522-9602
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 84
• Issue: 3
• Article Number: 39
• DOI: https://doi.org/10.1007/s11538-021-00982-5
• Keywords: Computational Theory and Mathematics; General Agricultural and Biological Sciences; Pharmacology; General Environmental Science; General Biochemistry, Genetics and Molecular Biology; General Mathematics; Immunology; General Neuroscience
• Public URL: https://nottingham-repository.worktribe.com/output/7222337
• Publisher URL: https://link.springer.com/article/10.1007/s11538-021-00982-5