The virtual heart as a platform for screening drug cardiotoxicity

Abstract

To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug-ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug-ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi-physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro-arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro-arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed.

Figure 1

Schematic illustration of the modulated receptor theory and guarded receptor theory on the HH type of Na⁺ ion channel. Figure adapted from Comtois et al. (2008). (A) Modulated receptor model proposed by Hondeghem and Katzung (1977) with transition rates from unblocked to blocked channels (k) and from blocked to unblocked (l). (B) Guarded receptor model with affinity to the inactivated and activated states (Starmer and Grant, 1985).

Figure 2

Assessment flow chart for testing drug actions using hierarchical levels of computer models including ion channel, cellular and tissue levels. The actions of a drug on cardiac electrical activity at cellular and tissue levels can be characterized by analysing their effects on a family of biomarkers.

Figure 3

Simulation of actions of cisapride and amiodarone on human epicardial ventricular APs with comparison to experimental data. Effects of a combined action of blocking of I_Kr by the two drugs at high and low doses together with different blocking of I_CaL (from 10 to 70%) were also shown. (Ai, Aii) Actions of cisapride and amiodarone at low doses. (Bi, Bii) Actions of cisapride and amiodarone at high doses. (Ci, Cii) Comparison of simulated APD prolongation results to experimental data for cisapride (Di Diego et al., 2003) and amiodarone (Nakagawa et al., 2010).

Figure 4

APD restitution curves computed from the epicardial cell model in the control condition and actions of low and high doses of cisapride and amiodarone. Effects of a combined block of I_Kr by the two drugs at high and low doses together with the different blocking of I_CaL (from 10 to 70%) are also shown. (A) low dose of cisapride; (B) high dose of cisapride; (C) low dose of amiodarone; (D) high dose of amiodarone.

Figure 5

Computed effects of cisapride and amiodarone on conduction velocity restitution curves of cardiac excitation waves at low and high doses. Effects of a combined action of blocking of I_Kr by the two drugs at high and low doses together with different blocking of I_CaL (from 10 to 70%) are also shown. (A) low dose of cisapride; (B) high dose of cisapride; (C) low dose of amiodarone; (D) high dose of amiodarone.

Figure 6

Computed wavelength restitution curves of cardiac excitation waves in control and cisapride and amiodarone conditions. Effects of a combined action of blocking of I_Kr by the two drugs at high and low doses together with different blocking of I_CaL (from 10% to 70%) are also shown. (A) low dose of cisapride; (B) high dose of cisapride; (C) low dose of amiodarone; (D) high dose of amiodarone.

Figure 7

(A) Computed width of vulnerable window of cardiac tissue in control and cisapride and amiodarone conditions. (B) Comparison of vulnerable window for cisapride at a low dose, basal I_Kr blocking (by 30%) of cisapride together with additional blocking of I_CaL by 10 and 30%.

Figure 8

(Left panel) Computed pseudo‐ECG in control and cisapride and amiodarone conditions. Both drugs prolonged QT interval. (Right panel) Comparison of simulated QT interval prolongation to experimental data for cisapride (Di Diego et al., 2003) and amiodarone (Varro et al., 1996).

Figure 9

Initiation and maintenance of re‐entry in a three‐dimensional realistic model of human ventricles under control (A), high cisapride (B), amiodarone (C) conditions. (Ai, Bi, Ci) Snapshots of conduction pattern of ventricular re‐entry. (Aii, Bii, Cii) Time series of electrical activity recorded from a local site in the ventricle. (Ci, Cii, Ciii) Power spectrum of the recorded electrical activities.

Figure 10

Schematic illustration of virtual heart as a platform for drug safety assessment.