Prediction of Thorough QT study results using action potential simulations based on ion channel screens

Abstract

Introduction: Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development.

Methods: Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations.

Results: We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity.

Discussion: This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment.

Keywords: Action potential; Cardiac safety; Compound screening; High-throughput; Mathematical model; Methods; Thorough QT.

Fig. 1

An overview of the steps involved in this study. Ion channel concentration-effect data are taken from a number of screening sources, then used to calculate percentage reduction for the parameters describing maximum conductance of the currents in a human in-silico action potential model. Steady pacing at 1Hz is used to simulate an APD₉₀ comparable with QTc. The process is repeated across a range of concentrations, and compared with the TQT study result at the relevant estimated concentration. The various steps are discussed in Methods 2.1, 2.2, 2.3, 2.4, 2.5.

Fig. 2

The behaviour of the three human ventricular action potential models used in this study under single current block. Each panel shows the steady state 1 Hz action potential under control (bold line), and increasing degrees of block, from 0% to 100% in steps of 10%. Rows: block of I_Kr, I_Ks, I_CaL, I_Na, I_to or I_K1; columns: ten Tusscher and Panfilov (2006), Grandi et al. (2010) or O'Hara et al. (2011) models. Arrows indicate the effect on the action potential waveform of increasing channel block.

Fig. 3

Simulated change in action potential duration (90%) plotted against (free plasma) concentrations. Models: Blue — O'Hara; red — ten Tusscher; green — Grandi. Three data sources are shown for: ‘Q’ (Quattro); ‘B & Q2’ (Barracuda & Quattro); ‘M & Q’ (Manual hERG & Quattro), as per Table 1. Estimated 95% credible regions are shown around each line which capture uncertainty due to screening assay variability. The clinical study result is shown with a black dashed horizontal line for the largest dose in the TQT study; the estimated free plasma concentration associated with this is shown with a vertical dashed black line, and their intersection with a red circle. The 5 ms ‘cut-off’, used in contingency table calculations, is shown with a horizontal blue dotted line.

Fig. 4

Simulated change in action potential duration (90%) plotted against (free plasma) concentrations. Legend as per Fig. 3.