Original Research ARTICLE
Global sensitivity analysis of ventricular myocyte model-derived metrics for proarrhythmic risk assessment
- 1IBM Research (United States), United States
Multiscale computational models of 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
(qN et) 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, qN et 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 qN et or a metric directly based on simulated EADs. In particular, the higher sensitivity
of qN et 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 qN et 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.
Keywords: Torsade de Pointes, computational modeling, Ion Channel Pharmacology, Global sensifivity analysis, Early afterdeloparizations
Received: 17 Apr 2019;
Accepted: 20 Aug 2019.
Copyright: © 2019 Parikh, Di Achille, Kozloski and Gurev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Viatcheslav Gurev, IBM Research (United States), Yorktown Heights, 10598, New York, United States, firstname.lastname@example.org