Optimization of an In Silico Cardiac Cell Model for Proarrhythmia Risk Assessment

Abstract

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.

Arrhythmic hazard map for a 3D whole‐ventricle model under multiple ion channel block

“…To our knowledge, however, this is the first application of such a simulation in biomedicine. We recognize that studies using a single-cell model of electrophysiology have reported excellent risk prediction (Mirams et al, 2011;Dutta et al, 2017), but comparisons with the clinical data ( Figure 2), evaluation of various ECG indices ( Figure 6) and examinations of heart morphology (Supporting Information Figure S2.32) can be made only with the multiscale simulation. We are aware of those studies examining the ECG changes using one-dimensional models in which transmural distribution of different cell speciesthat is, endocardial, mid-myocardial and epicardial cellsare reproduced (Bottino et al, 2006;Suzuki et al, 2008).…”

“…Because of limited computational resources, we simulated only five beats for each case. Studies using a single-cell model repeated the simulation for 1000 cycles to reach steady state (Dutta et al, 2017). Also, using a ventricular model, Sadriegh et al showed that the convergence of T-wave morphology requires more than 100 beats (Sadrieh et al, 2014).…”

“…Although evaluations were made only at ETPC unbound , these results suggest that the evaluation of arrhythmogenicity is acceptable before reaching steady state (quasi-steady state). In fact, in recent studies using single-cell models (Dutta et al, 2017;Kurata et al, 2017), the authors examined the occurrence of EAD at fewer than 1000 beats (quasi-steady state). Duttas et al found a strong correlation between their metrics of arrhythmogenicity (net charge constituting the net current during the stimulated beat) analysed at 1000 beats, and the I Kr reduction threshold for the induction of EAD checked at 100 beats (Dutta et al, 2017).…”

“…In fact, in recent studies using single-cell models (Dutta et al, 2017;Kurata et al, 2017), the authors examined the occurrence of EAD at fewer than 1000 beats (quasi-steady state). Duttas et al found a strong correlation between their metrics of arrhythmogenicity (net charge constituting the net current during the stimulated beat) analysed at 1000 beats, and the I Kr reduction threshold for the induction of EAD checked at 100 beats (Dutta et al, 2017). Similarly, Kurata et al evaluated the occurrence of EAD with 0.5-1.0 min of calculation, whereas they required 5-30 min to achieve steady state.…”

Drug‐induced Proarrhythmia and Torsade de Pointes: A Primer for Students and Practitioners of Medicine and Pharmacy

“…Additional references are provided. [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82]…”