Protocol-dependent differences in IC<sub>50</sub> values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding

Lee, William; Windley, Monique J.; Perry, Matthew D.; Vandenberg, Jamie I.; Hill, Adam P.

doi:10.1101/534867

Cited by 7 publications

(8 citation statements)

References 28 publications

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“…We have seen, though, that method 4 can provide excellent simulations of these protocols, so comparisons to previous data could be made by 1) running an experiment with a novel optimized protocol, 2) quickly and reliably fitting a model to the raw current data, and 3) simulating the previously used protocols and performing the analysis. Indeed, this method could be used to compare data sets (via modeling) from protocols with slight discrepancies ( 90 ).…”

Section: Discussionmentioning

confidence: 99%

Four Ways to Fit an Ion Channel Model

Clerx

Beattie

Gavaghan

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.

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Section: Discussionmentioning

confidence: 99%

Four Ways to Fit an Ion Channel Model

Clerx

Beattie

Gavaghan

et al. 2019

Biophysical Journal

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“…This is especially important because estimates of drug potency (e.g., IC50) are highly dependent on the patch clamp experimental methods used, and differences in voltage protocols, temperatures, ion channel expression systems, and quality control standards can lead to very different potency estimates across laboratories. 9,[50][51][52][53][54] The second implication is that data used for any new compound predictions by a given model will need to be generated using the same experimental protocols as those used for the model training and validation compounds. Otherwise, the new drug may be outside of the applicability domain of the model, and thus reliability of predictions cannot be established.…”

Section: Principle 3: a Defined Domain Of Applicabilitymentioning

confidence: 99%

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

Mirams

Yoshinaga

et al. 2019

Clin Pharma and Therapeutics

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This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/ US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.In July 2013, a Think Tank jointly sponsored by Cardiac Safety Research Consortium (CSRC), Health and Environmental Sciences Institute (HESI), and the US Food and Drug Administration (FDA) proposed a new cardiac safety paradigm, Comprehensive in Vitro Proarrhythmia Assay (CiPA). CiPA uses a new mechanistic, model-informed approach to predict the risk of Torsade de Pointes (TdP), a rare but potentially lethal form of ventricular tachycardia that can be induced by drugs and lead to sudden death. 1 Since its inception, global stakeholders including regulatory agencies (the FDA, European Medicines Agency, Health Canada, and the Japan Pharmaceuticals and Medical Devices Agency), industry, and academia have assembled various

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“…We have seen though, that Method 4 can provide excellent simulations of these protocols, so that comparisons to previous data could be made by (1) running an experiment with a novel optimised protocol, (2) quickly and reliably fitting a model to the raw current data, (3) simulating the previously used protocols and performing the analysis. Indeed, this method could be used to compare data sets (via modelling) from protocols with slight discrepancies (82).…”

Section: Discussionmentioning

confidence: 99%

Four ways to fit an ion channel model

Clerx

Beattie

Gavaghan

et al. 2019

Preprint

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Computational models of the cardiac action potential are increasingly being used to investigate the effects of 4 genetic mutations, predict pro-arrhythmic risk in drug development, and to guide clinical interventions. These safety-critical 5 applications, and indeed our understanding of the cardiac action potential, depend on accurate characterisation of the underlying 6 ionic currents. Four different methods can be found in the literature to fit ionic current models to single-cell measurements: 7 (Method 1) fitting model equations directly to time constant, steady-state, and I-V summary curves; (Method 2) fitting by comparing 8 simulated versions of these summary curves to their experimental counterparts; (Method 3) fitting to the current traces themselves 9 from a range of protocols; and (Method 4) fitting to a single current trace from an information-rich voltage clamp protocol. We 10 compare these methods using a set of experiments in which hERG1a current from single Chinese Hamster Ovary (CHO) cells 11 was characterised using multiple fitting protocols and an independent validation protocol. We show that Methods 3 and 4 provide 12 the best predictions on the independent validation set, and that the short information-rich protocols of Method 4 can replace 13 much longer conventional protocols without loss of predictive ability. While data for Method 2 is most readily available from the 14 literature, we find it performs poorly compared to Methods 3 and 4 both in accuracy of predictions and computational efficiency. 15 Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in 16 safety-critical applications. 17 Statement of Significance 18Mathematical models have been constructed to capture and share our understanding of the kinetics of ion channel currents 19 for almost 70 years, and hundreds of models have been developed, using a variety of techniques. We compare how well four 20 of the main methods fit data, how reliable and efficient the process of fitting is, and how predictive the resulting models are 21 for physiological situations. The most widely-used traditional approaches based on current-voltage and time constant-voltage 22 curves do not produce the most predictive models. Short, optimised experimental voltage clamp protocols can be used to create 23 models that are as predictive as ones derived from traditional protocols, opening up possibilities for measuring ion channel 24 kinetics faster, more accurately and in single cells. As these models often form part of larger multi-scale action potential and 25 tissue electrophysiology models, improved ion channel kinetics models could influence the findings of thousands of simulation 26 studies. 27Computational models of ionic currents have been used to understand the formation of the action potential (AP) (1-3), the 29 effects of genetic mutations (4), and the action of drugs on the rhythm of the heart (5). By fitting a mathematical model to 30 experimentally measured currents w...

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Protocol-dependent differences in IC₅₀ values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding

Cited by 7 publications

References 28 publications

Four Ways to Fit an Ion Channel Model

Four Ways to Fit an Ion Channel Model

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

Four ways to fit an ion channel model

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Protocol-dependent differences in IC50 values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding

Cited by 7 publications

References 28 publications

Four Ways to Fit an Ion Channel Model

Four Ways to Fit an Ion Channel Model

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

Four ways to fit an ion channel model

Contact Info

Product

Resources

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Protocol-dependent differences in IC₅₀ values measured in hERG assays occur in a predictable way and can be used to quantify state preference of drug binding