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

Abstract

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.

Assessment of proarrhythmogenic risk for chloroquine and hydroxychloroquine using the CiPA concept

“…Q net values found by us for the two antimalarials ( Table S31 of Supplemental files) are similar to those reported by ( Delaunois et al, 2021 ; Whittaker et al, 2021 ), along with APD-prolongation and EAD-induction effects found in other simulation studies ( Montnach et al, 2021 ; Varshneya et al, 2021 ). Although Q net provides an in-depth mechanistic interpretation of proarrhythmogenic effects ( Parikh et al, 2019 ), we found that the method requires supplementary precautions and avoiding simulated APs that actually feature EADs.…”

Circadian rhythms of early afterdepolarizations and ventricular arrhythmias in a cardiomyocyte model

“…ERG block is a sensitive but not specific measure of TdP risk, i.e. it gives few false negatives but false positives may be preventing safe drugs from entering the market [50]. The Comprehensive in vitro Pro-arrhythmia Assay (CiPA) is a new global initiative to create guidelines for the assessment of drug-induced TdP that recommends a central role for computational modeling of ion channels and in silico evaluation of compounds [19,40].…”

Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action

“…We performed global sensitivity analysis to systematically examine the influence of model input parameters on various model-derived metrics. Mean Decrease Accuracy (MDA) was used for estimation of sensitivity, as in our previous study [ 38 ]. Briefly, sensitivity analysis via MDA requires fitting a linear or non-linear regression model (e.g., linear regression model, random forest regression model, etc.)…”