ArticleFrontiers in physiology2016
Parameter Identifiability of Fundamental Pharmacodynamic Models.
Article in Frontiers in physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Predicting QRS and PR interval prolongations in humans using nonclinical data.British journal of pharmacology · 2017Trial
- On the structural identifiability of nonlinear models of ligand binding dynamics.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- Diffusion dimensionality modeling of subcutaneous/intramuscular absorption of antibodies and long-acting injectables.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding.Analytical biochemistry · 2023Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Quantifying tissue growth, shape and collision via continuum models and Bayesian inference.Journal of the Royal Society, Interface · 2023Article
- Benchmarking tools for a priori identifiability analysis.Bioinformatics (Oxford, England) · 2023Article
- Structural identifiability of compartmental models for infectious disease transmission is influenced by data type.Epidemics · 2022Article
- Explicit Consideration of Temperature Improves Predictions of Toxicokinetic-Toxicodynamic Models for Flupyradifurone and Imidacloprid inEnvironmental science & technology · 2022Article
- Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Profile likelihood analysis for a stochastic model of diffusion in heterogeneous media.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- Population Model of Serum Creatinine as Time-Dependent Covariate in Neonates.The AAPS journal · 2021Article
- Structural identifiability and observability of compartmental models of the COVID-19 pandemic.Annual reviews in control · 2021Review
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models.The AAPS journal · 2020Article
- Practical parameter identifiability for spatio-temporal models of cell invasion.Journal of the Royal Society, Interface · 2020Article
- A primer on model selection using the Akaike Information Criterion.Infectious Disease Modelling · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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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