Rapid characterisation of hERG channel kinetics II: temperature dependence

et al. 2019

Biophysical Journal

Self Cite

102

Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug development and requires a deep understanding of a compound’s action on ion channels. In vitro hERG channel current recordings are an important step in evaluating the proarrhythmic potential of small molecules and are now routinely performed using automated high-throughput patch-clamp platforms. These machines can execute traditional voltage-clamp protocols aimed at specific gating processes, but the array of protocols needed to fully characterize a current is typically too long to be applied in a single cell. Shorter high-information protocols have recently been introduced that have this capability, but they are not typically compatible with high-throughput platforms. We present a new 15 second protocol to characterize hERG (Kv11.1) kinetics, suitable for both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, by applying it to Chinese hamster ovary cells stably expressing hERG1a. From these recordings, we construct 124 cell-specific variants/parameterizations of a hERG model at 25°C. A further eight independent protocols are run in each cell and are used to validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we use to quantify the uncertainty in the model parameters, and their variability from cell-to-cell; we use this model to suggest reasons for the variability. This study demonstrates a robust method to measure and quantify uncertainty and shows that it is possible and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly.

Section: Discussionmentioning

confidence: 99%

Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System

Lei

et al. 2019

Biophysical Journal

Self Cite

102

“…Finally, if we were to believe that the observed variability here arises from experimental artefacts, then only the uncertainty in the top-level mean parameter vector µ in the hierarchical Bayesian model is representative of our uncertainty in the underlying physiology. That is, the variability of top-level mean parameter vector µ should be included in future physiological studies, for example in the second part of this study Lei et al (27), or when embedding an ion channel model within an action potential model, whilst the full posterior predictive distribution should be used only when predicting the results of future patch clamp experiments .…”

Section: Discussionmentioning

confidence: 99%

Rapid characterisation of hERG channel kinetics I: using an automated high-throughput system

Lei

et al. 2019

Preprint

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1 Affiliations removed for initial submission as per guidelines. 6 ABSTRACT Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug-development, and requires a 7 deep understanding of a compound's action on ion channels. In vitro hERG-channel current recordings are an important step in 8 evaluating the pro-arrhythmic potential of small molecules, and are now routinely performed using automated high-throughput 9 patch clamp platforms. These machines can execute traditional voltage clamp protocols aimed at specific gating processes, 10 but the array of protocols needed to fully characterise a current is typically too long to be applied in a single cell. Shorter 11 high-information protocols have recently been introduced which have this capability, but they are not typically compatible with 12 high-throughput platforms. We present a new high-information 15 s protocol to characterise hERG (Kv11.1) kinetics, suitable for 13 both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384 well automated 14 patch clamp platform, by applying it to CHO cells stably expressing hERG1a. From these recordings we construct 124 cell-specific 15 variants/parameterisations of a hERG model at 25 • C. A further 8 independent protocols are run in each cell, and are used to 16 validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we 17 use to quantify the uncertainty in the model parameters, and their variability from cell to cell, which we use to suggest reasons 18 for the variability. This study demonstrates a robust method to measure and quantify uncertainty, and shows that it is possible 19 and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly. 20 21 We present a method for high-throughput characterisation of hERG potassium channel kinetics, via fitting a mathematical 22 model to results of over one hundred single cell patch clamp measurements collected simultaneously on an automated voltage 23 clamp platform. The automated patch clamp data are used to parameterise a mathematical ion channel model fully, opening a 24 new era of automated and rapid development of mathematical models from quick and cheap experiments. The method also 25 allows ample data for independent validation of the models and enables us to study experimental variability and propose its 26 origins. In future the method can be applied to characterise changes to hERG currents in different conditions, for instance 27 at different temperatures (see Part II of the study) or under mutations or the action of pharmaceuticals; and should be easily 28 adapted to study many other currents. Statement of Significance 30The human Ether-à-go-go-Related Gene (hERG) is of great interest in the field of cardiac safety pharmacology. hERG encodes 31 the pore-forming alpha subunit of the ion channel Kv11.1 which conducts the rapid delayed rectifier potassium current, I Kr 32(1)...

“…The voltage dependencies of the transition rates are defined using an Eyring-derived exponential formulation ( 53 , 70 , 71 , 72 ) as

…”

Section: Methodsmentioning

confidence: 99%

Four Ways to Fit an Ion Channel Model

Beattie

et al. 2019

Biophysical Journal

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Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.