A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability

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.

Gradient-based parameter optimization to determine membrane ionic current composition of human induced pluripotent stem cell-derived cardiomyocytes

“…Parameter identifiability has been one of the central issues in the parameter optimization of biological models [14,20]. For confirmation of the identifiability of a unique set of solutions using the parameter optimization method, mapping of the MSE distribution is required over an enlarged parameter space defined by the sf x of the nine ionic currents of the baseline model.…”

“…The automatic parameter optimization technique has been used to determine parameters objectively in a wide range of various biological models (in cardiac electrophysiology; [12][13][14][15], in the systems pharmacology; [16][17][18][19][20]). Because of this utility, a large variety of improvements have been made in the area of information technology [21,22].…”

Ion channel model reduction using manifold boundaries

“…This approach is relatively common not just in the field of cardiac modelling, but also in the relatively new discipline of quantitative systems pharmacology. The MBAM may therefore also be of great utility in this context, in which the desirability of models with identifiable parameters has begun to be appreciated [30].…”

“…Whilst this does reduce the dimensionality of the parameter search space, it does not make the model conceptually simpler or necessarily help to illuminate the connection between model parameters and output, unlike model reduction methods such as the MBAM. The MBAM may therefore also be of great utility in this context, in which the desirability of models with identifiable parameters has begun to be appreciated [32].…”

Gradient-based parameter optimization to determine membrane ionic current composition of human induced pluripotent stem cell-derived cardiomyocytes

“…Parameter identifiability has been one of the central issues in the parameter optimization of biological models [14,20]. For confirmation of the identifiability of a unique set of solutions using the parameter optimization method, mapping of the MSE distribution is required over an enlarged parameter space defined by the sfx of the nine ionic currents of the baseline model.…”