A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Article number39
JournalBulletin of Mathematical Biology
Volume84
Issue number3
Early online date7 Feb 2022
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Model development
  • Model identifiability
  • Quantitative systems pharmacology

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