Parameter Identifiability of Fundamental Pharmacodynamic Models
Open accessAbstract
Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably "good" standard errors may sometimes mask unidentifiability issues.
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Software packages for structural identifiability analysis have been developed in recent years and are generally widely available.
“During the past few years, software packages have been developed and are generally widely available that can ease the burden in performing such analysis.52, 53, 54, 55”
From Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network (2019) · cites this paper as [54]
The Methods section (Investigated model structures; page not provided) names DAISY as software implementing a structural-identifiability approach and mentions other approaches that can ease analysis. It does not say that software packages were developed in recent years or are generally widely available. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRecent techniques for structural identifiability analysis of mixed-effects models widen the scope of applying such analysis to QSP models.
“The introduction of recent techniques for the structural identifiability analysis of mixed‐effects models (commonly applied in a population PKPD context) widens the scope of the application of such analysis to QSP models.54”
From Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network (2019) · cites this paper as [54]
The Methods section describes an extension of structural identifiability analysis from fixed-effects to mixed-effects models, and the Results apply it to pharmacodynamic models. Our copy does not say that this widens the scope of analysis to QSP models specifically. Page numbers are unavailable in our copy. (LLM, conf=0.82)
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- Authors
- Janzén, D. L. I., Bergenholm, L., Jirstrand, M., Parkinson, J., Yates, J., Evans, N. D., & Chappell, M. J.
- Affiliations
- University of Warwick (GB)
- Fraunhofer Chalmers Research Centre for Industrial Mathematics (SE)
- AstraZeneca (Sweden) (SE)
- AstraZeneca (United Kingdom) (GB)
- Corresponding author
- Janzén, David L. I.
- Bergenholm, Linnéa
- ORCID
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2016-12-05
- Volume
- 7
- Pages
- 590-590
- ISSN
- 1664-042X (ISSN-L)
- PMC
- PMC5136565
- OpenAlex
- W2552635486
- Cited by
- 44
- References
- 28
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 1 · 2023: 6 · 2022: 5 · 2021: 6 · 2020: 6 · 2019: 6 · 2018: 3 · 2017: 5
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Physiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC/K001205/1)
- Engineering and Physical Sciences Research Council (EP/L000296/1)
- FP7 People: Marie-Curie Actions (316736)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- parameter identifiability, structural identifiability, pharmacodynamic modeling, input-output method, mixed models, between-subject variability, data quality, parameter estimation, standard errors, model development
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