Dataset Information

Automated Algorithm for J-Tpeak and Tpeak-Tend Assessment of Drug-Induced Proarrhythmia Risk.

ABSTRACT:

Background

Prolongation of the heart rate corrected QT (QTc) interval is a sensitive marker of torsade de pointes risk; however it is not specific as QTc prolonging drugs that block inward currents are often not associated with torsade. Recent work demonstrated that separate analysis of the heart rate corrected J-Tpeakc (J-Tpeakc) and Tpeak-Tend intervals can identify QTc prolonging drugs with inward current block and is being proposed as a part of a new cardiac safety paradigm for new drugs (the "CiPA" initiative).

Methods

In this work, we describe an automated measurement methodology for assessment of the J-Tpeakc and Tpeak-Tend intervals using the vector magnitude lead. The automated measurement methodology was developed using data from one clinical trial and was evaluated using independent data from a second clinical trial.

Results

Comparison between the automated and the prior semi-automated measurements shows that the automated algorithm reproduces the semi-automated measurements with a mean difference of single-deltas <1 ms and no difference in intra-time point variability (p for all > 0.39). In addition, the time-profile of the baseline and placebo-adjusted changes are within 1 ms for 63% of the time-points (86% within 2 ms). Importantly, the automated results lead to the same conclusions about the electrophysiological mechanisms of the studied drugs.

Conclusions

We have developed an automated algorithm for assessment of J-Tpeakc and Tpeak-Tend intervals that can be applied in clinical drug trials. Under the CiPA initiative this ECG assessment would determine if there are unexpected ion channel effects in humans compared to preclinical studies. The algorithm is being released as open-source software.

Trial registration

NCT02308748 and NCT01873950.

SUBMITTER: Johannesen L

PROVIDER: S-EPMC5201230 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated Algorithm for J-Tpeak and Tpeak-Tend Assessment of Drug-Induced Proarrhythmia Risk.

Johannesen Lars L Vicente Jose J Hosseini Meisam M Strauss David G DG

PloS one 20161230 12

<h4>Background</h4>Prolongation of the heart rate corrected QT (QTc) interval is a sensitive marker of torsade de pointes risk; however it is not specific as QTc prolonging drugs that block inward currents are often not associated with torsade. Recent work demonstrated that separate analysis of the heart rate corrected J-Tpeakc (J-Tpeakc) and Tpeak-Tend intervals can identify QTc prolonging drugs with inward current block and is being proposed as a part of a new cardiac safety paradigm for new d ...[more]

PMID: 28036330

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Project description:Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm.

Project description:The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax). However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was <60%, preventing reliable estimation of IC50-values. The results of this study demonstrate that the accuracy of TdP risk prediction depends both on the intrinsic variability in ion channel pharmacology data as well as on experimental design considerations that preclude an accurate determination of drug IC50-values in vitro. Thus, we demonstrate that UQ provides valuable information about in silico modeling predictions that can inform future proarrhythmic risk evaluation of drugs under the CiPA paradigm.

Dataset Information

Automated Algorithm for J-Tpeak and Tpeak-Tend Assessment of Drug-Induced Proarrhythmia Risk.

Background

Methods

Results

Conclusions

Trial registration

Publications

Automated Algorithm for J-Tpeak and Tpeak-Tend Assessment of Drug-Induced Proarrhythmia Risk.

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