Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator

McMillan, Beth; Gavaghan, David; Mirams, Gary R.

doi:10.1039/c7tx00141j

Cited by 17 publications

(7 citation statements)

References 64 publications

(91 reference statements)

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“…We also identify the inputs that are important for classifying virtual drugs into different risk groups based either on an EAD metric or on qNet . In agreement with a recent report (McMillan et al, 2017) showing better performance of classifiers built on simple metrics such as APD 90, we find that qNet performs better than the EAD metric in classifying torsadogenic risk. Our results indicate that, despite being well correlated to metrics directly based on EADs, qNet also depends on additional parameters that seem to confer its better performance.…”

Section: Introductionsupporting

confidence: 93%

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment

et al. 2019

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Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents (qNet) have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification. In particular, qNet has been recently proposed as a surrogate metric for early afterdepolarizations (EADs), which are known to be cellular triggers of TdP. Analysis of critical model components and of the ion channels that have major impact on model-derived metrics can lead to improvements in the confidence of the prediction. In this paper, we analyze large populations of virtual drugs to systematically examine the influence of different ion channels on model-derived metrics that have been proposed for proarrhythmic risk assessment. We demonstrate via global sensitivity analysis (GSA) that model-derived metrics are most sensitive to different sets of input parameters. Similarly, important differences in sensitivity to specific channel blocks are highlighted when classifying drugs into different risk categories by either qNet or a metric directly based on simulated EADs. In particular, the higher sensitivity of qNet to the block of the late sodium channel might explain why its classification accuracy is better than that of the EAD-based metric, as shown for a small set of known drugs. Our results highlight the need for a better mechanistic interpretation of promising metrics like qNet based on a formal analysis of models. GSA should, therefore, constitute an essential component of the in silico workflow for proarrhythmic risk assessment, as an improved understanding of the structure of model-derived metrics could increase confidence in model-predicted risk.

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Section: Introductionsupporting

confidence: 93%

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment

et al. 2019

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“…To incorporate drug effects into our multiscale models, we selectively block the relevant ionic currents in the Purkinje and cardiomyocyte cell models (Sahli Costabal et al, 2018b ). These blocks are informed by experimental patch-clamp experiments that study the fractional blockage β of different ion channels at varying drug concentrations (McMillan et al, 2017 ). We implement these fractional blockings using fitted Hill-type equations of the form,…”

Section: Methodsmentioning

confidence: 99%

“…Using our multi-fidelity arrhythmogenicity classification boundary, we estimate the arrhythmogenic risk of three drugs, a high, intermediate and low risk drug (Li et al, 2018 ), by computing the critical drug concentration at which arrhythmia will start developing. We select three drugs for which the concentration-block response curve is well-described (McMillan et al, 2017 ) for the two cardiac currents that have the most significant impact on arrhythmogenic risk prediction (section 2.3.1). The critical drug concentration is found at the intersection of the multi-fidelity arrhythmogenesis classification boundary and the two-dimensional concentration-block trajectory described by Equation (5).…”

Section: Methodsmentioning

confidence: 99%

Sex Differences in Drug-Induced Arrhythmogenesis

2021

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The electrical activity in the heart varies significantly between men and women and results in a sex-specific response to drugs. Recent evidence suggests that women are more than twice as likely as men to develop drug-induced arrhythmia with potentially fatal consequences. Yet, the sex-specific differences in drug-induced arrhythmogenesis remain poorly understood. Here we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmia at differing drug concentrations. To quantify critical drug concentrations in male and female hearts, we identify the most important ion channels that trigger male and female arrhythmogenesis, and create and train a sex-specific multi-fidelity arrhythmogenic risk classifier. Our study reveals that sex differences in ion channel activity, tissue conductivity, and heart dimensions trigger longer QT-intervals in women than in men. We quantify the critical drug concentration for dofetilide, a high risk drug, to be seven times lower for women than for men. Our results emphasize the importance of including sex as an independent biological variable in risk assessment during drug development. Acknowledging and understanding sex differences in drug safety evaluation is critical when developing novel therapeutic treatments on a personalized basis. The general trends of this study have significant implications on the development of safe and efficacious new drugs and the prescription of existing drugs in combination with other drugs.

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“…To select the compounds, we began with a list of 31 drugs ( 20 ) for which the concentration block is thoroughly characterized. From these 31 drugs, we only considered those for which 70% or more of the published studies agreed on their risk classification ( 40 , 41 ) and did not consider the remaining eight controversial drugs. Table 1 summarizes the IC 50 values used to compute the degree of blockade of the L-type calcium current I CaL and the rapid delayed rectifier potassium current I Kr ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning

Francisco

Seo

Ashley

et al. 2020

Biophysical Journal

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All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

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Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator

Abstract: A method of predicting drug-induced Torsade de Pointes risk based on the occurrence of simulated early after depolarisations.

Cited by 17 publications

References 64 publications

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment

Sex Differences in Drug-Induced Arrhythmogenesis

Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning

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