Local identifiability for two and three-compartment pharmacokinetic models with time-lags

Merino, José A.; Biasi, J. De; Plusquellec, Y.; Houin, Georges

doi:10.1016/s1350-4533(98)00015-0

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…A number of independent studies have tackled the identifiability problem for DDE models [4, 5, 27, 31, 66, 68, 79, 124, 125]. However, most of these previous works deal with very simple and specific DDE models (e.g., [68]) and the generalizability of these results are limited due to a lack of understanding of the important feature of DDE models: the propagation of discontinuity at t 0 or in the history functions from lower to higher order derivatives of state variables if there is any. Such a feature makes DDE models easily become bifurcated or just unsolvable [11, 41, 42].…”

Section: Discussionmentioning

confidence: 99%

On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics

Miao

Xia²,

Perelson³

et al. 2011

SIAM Rev.

512

578

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Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice.

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Section: Discussionmentioning

confidence: 99%

On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics

Miao

Xia²,

Perelson³

et al. 2011

SIAM Rev.

512

578

View full text Add to dashboard Cite

show abstract

Deterministic identifiability of population pharmacokinetic and pharmacokinetic–pharmacodynamic models

Siripuram

Wright

Barclay

et al. 2017

J Pharmacokinet Pharmacodyn

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Identifiability is an important component of pharmacokinetic-pharmacodynamic (PKPD) model development. Structural identifiability is concerned with the uniqueness of the model parameters for a set of perfect input-output data and deterministic identifiability with the precision of parameter estimation given imperfect input-output data. We introduce two subcategories of deterministic identifiability, external and internal, and consider factors that distinguish between these forms. We define external deterministic identifiability as a function of externally controllable variables, i.e., the design, and internal deterministic identifiability as a function of the model and its parameter values. The concepts are explored using three common PK and PKPD models, and verified for their precision for the selected set of parameter values under optimal design.

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Local identifiability for two and three-compartment pharmacokinetic models with time-lags

Cited by 2 publications

References 15 publications

On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics

On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics

Deterministic identifiability of population pharmacokinetic and pharmacokinetic–pharmacodynamic models

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