Personalization of Cellular Electrophysiology Models: Utopia?

WIREs Mechanisms of Disease

et al. 2020

Self Cite

Cardiac electrophysiology models are among the most mature and well‐studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models

Section: Patient-specific Modelsmentioning

confidence: 99%

Calibration of ionic and cellular cardiac electrophysiology models

Whittaker

WIREs Mechanisms of Disease

et al. 2020

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“…( 4 ) to extrapolate room temperature recordings to physiological temperature. These extrapolations cause considerable changes to rates, often exceeding changes introduced when modeling diseases or other conditions ( 37 ). Similarly, the Christé et al.…”

Section: Introductionmentioning

confidence: 99%

Rapid Characterization of hERG Channel Kinetics II: Temperature Dependence

Beattie

et al. 2019

Biophysical Journal

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Ion channel behavior can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel kinetics (rates of opening, closing, inactivating, and recovery) are commonly represented with Q 10 coefficients or an Eyring relationship. In this article, we assess the validity of these representations by characterizing channel kinetics at multiple temperatures. We focus on the human Ether-à-go-go-Related Gene (hERG) channel, which is important in drug safety assessment and commonly screened at room temperature so that results require extrapolation to physiological temperature. In Part I of this study, we established a reliable method for high-throughput characterization of hERG1a (Kv11.1) kinetics, using a 15-second information-rich optimized protocol. In this Part II, we use this protocol to study the temperature dependence of hERG kinetics using Chinese hamster ovary cells overexpressing hERG1a on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, with temperature control. We characterize the temperature dependence of hERG gating by fitting the parameters of a mathematical model of hERG kinetics to data obtained at five distinct temperatures between 25 and 37°C and validate the models using different protocols. Our models reveal that activation is far more temperature sensitive than inactivation, and we observe that the temperature dependency of the kinetic parameters is not represented well by Q 10 coefficients; it broadly follows a generalized, but not the standardly-used, Eyring relationship. We also demonstrate that experimental estimations of Q 10 coefficients are protocol dependent. Our results show that a direct fit using our 15-s protocol best represents hERG kinetics at any given temperature and suggests that using the Generalized Eyring theory is preferable if no experimental data are available to derive model parameters at a given temperature.

“…Many cardiac action potential models (32–35) adapted the Mazhari et al (36) hERG model which used Q 10 values from Zhou et al (4) to extrapolate room temperature recordings to physiological temperature. These extrapolations cause considerable changes to rates, often exceeding changes introduced when modelling diseases or other conditions (37). Similarly, the Christé et al (38) hERG model was based on measurements at room temperature and extrapolated to 37 °C using Q 10 values from Vandenberg et al (3).…”

Section: Introductionmentioning

confidence: 99%

Rapid characterisation of hERG channel kinetics II: temperature dependence

Beattie

et al. 2019

Preprint

Self Cite

1 Affiliations removed for initial submission as per guidelines. 6 ABSTRACT Ion channel behaviour can depend strongly on temperature, with faster kinetics at physiological temperatures 7 leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-8 dependent ion channel kinetics (rates of opening, closing and inactivating) are commonly represented with Q 10 coefficients 9 or an Eyring relationship. In this paper we assess the validity of these representations by characterising channel kinetics at 10 multiple temperatures. We focus on the hERG channel, which is important in drug safety assessment and commonly screened 11 at room temperature, so that results require extrapolation to physiological temperature. In Part I of this study we established a 12 reliable method for high-throughput characterisation of hERG1a (Kv11.1) kinetics, using a 15 second information-rich optimised 13 protocol. In this Part II, we use this protocol to study the temperature dependence of hERG kinetics using CHO cells over-14 expressing hERG1a on the Nanion SyncroPatch 384PE, a 384-well automated patch clamp platform, with temperature control. 15 We characterise the temperature dependence of hERG gating by fitting the parameters of a mathematical model of hERG kinetics 16 to data obtained at five distinct temperatures between 25 and 37 • C, and validate the models using different protocols. Our models 17 reveal that activation is far more temperature sensitive than inactivation, and we observe that the temperature dependency of 18 the kinetic parameters is not represented well by Q 10 coefficients: it broadly follows a generalised, but not the standardly-used, 19 Eyring relationship. We also demonstrate that experimental estimations of Q 10 coefficients are protocol-dependent. Our results 20 show that a direct fit using our 15 second protocol best represents hERG kinetics at any given temperature, and suggests that 21 predictions from the Generalised Eyring theory may be preferentially used if no experimentally-derived data are available. 22 Statement of Significance 23Ion channel currents are highly sensitive to temperature changes. Yet because many experiments are performed more easily at 24 room temperature, it is common to extrapolate findings to physiological temperatures through the use of Q 10 coefficients or 25 Eyring rate theory. By applying short, information-rich protocols that we developed in Part I of this study we identify how 26 kinetic parameters change over temperature. We find that the commonly-used Q 10 and Eyring formulations are incapable of 27 describing the parameters' temperature dependence, a more Generalised Eyring relationship works well, but remeasuring 28 kinetics and refitting a model is optimal. The findings have implications for the accuracy of the many applications of Q 10 29 coefficients in electrophysiology, and suggest that care is needed to avoid misleading extrapolations in their many scientific and 30 industrial pharmaceutica...