Optimization of an In Silico Cardiac Cell Model for
Proarrhythmia Risk Assessment
Sara Dutta1, David Strauss1, Thomas Colatsky2, Zhihua Li1
1

U.S. Food and Drug Administration, Silver Spring, MD, USA; 2 Seabrook Island SC, USA
Abstract

The Comprehensive in vitro Proarrhythmia Assay(CiPA) is a regulatory paradigm proposed to replace theICH S7B and E14 guidelines for assessing drug-induced
proarrhythmia. Under CiPA, drug effects on multiple
cardiac ion channels will be measured in vitro andintegrated into an in silico model of the adult humanventricular cell, based on the O’Hara-Rudy (ORd) model.
However, the ORd model does not accurately represent
certain ionic currents known to be critical in triggeringdrug-induced arrhythmias, such as the late sodium
current (INaL). The goal of the present study is tosystematically assess and improve the simulation of themain depolarizing and repolarizing ionic currents (theinward rectifying potassium currents, L-type calcium
current and INaL) in the ORd model. We present a newmodel with scaled conductances calculated by fitting to
O’Hara et al. in vitro human cardiomyocyte channel
blocking experiments using a genetic algorithm, which
improves discrepancies of the original model. The
modified model particularly improves the effect of INaL
block on action potential prolongation, an important
determinant of proarrhythmia risk in the context of CiPA.

1.

Introduction

Torsade-de-pointes (TdP) is a lethal type of arrhythmiathat caused removal of several drugs from the market [1]and led to the adoption of the ICH E14 and S7B
guidelines to identify TdP risk. Although block of thedelayed rectifier potassium current (IKr) and the 10 ms
prolongation of QT criteria set by the guidelines arehighly sensitive predictors of TdP risk, they are notspecific and may prevent many useful and effective drugs
with low TdP risk from entering the market. In an effort
to improve the specificity of assessing clinical TdP risk,
the Comprehensive in vitro Proarrhythmia Assay (CiPA)
collaborative initiative was established [2]. The aim of
this regulatory paradigm is to combine measurements ofmultiple cardiac ionic currents with in silico modeling topredict TdP risk, coupled with stem cell and clinical ECG
ISSN: 2325-887X

studies to confirm those predictions or identify
mechanisms missing from the patch clamp data sets. The
O’Hara et al. model (ORd) was chosen as the consensus
base model [3].
As part of CiPA, an initial set of 12 drugs determinedby cardiologists to have low, intermediate or high risk of
TdP were selected to form a training set. Many of the
CiPA drugs are multi-channel blockers, particularly the
low risk drugs [4]–[6]. However, simulations of drug
effects using the current version of the ORd model andlatest patch clamp data show discrepancies with results onhuman cardiomyocytes, suggesting some currents (eg. thelate sodium current, INaL) are not correctly represented.
The aim of this study is to improve the ORd model sothat it accurately simulates the changes in action potential
duration (APD) produced by drugs that block multiple ionchannels.

2.

Methods

2.1.

Simulation protocol

All simulations were run using the ORd endocardial
cell model for 1000 beats at varying cycle lengths (CLs)
500, 1000, 2000 and 4000 ms. Drug block was simulatedusing the Hill function and data from Crumb et al. [7] forvarying drug concentrations: free plasma clinical drug
exposures (Cmax) up to 20X Cmax. APD was calculated
as the time taken for the transmembrane potential (Vm) of
the cell to reach 90% (APD90), 70% (APD70), 50%(APD50) and 30% (APD30) of its resting Vm. ΔAPD was
calculated as the percentage difference in APD
prolongation between control and drug. Simulations wererun in R and C using the deSolve package.

2.2.

Modified ORd model

The original ORd model was modified by scaling
conductances as follows: IKr by 1.119, the slow rectifierpotassium current (IKs) by 1.648, the inwardly rectifying
potassium current (IK1) by 1.414, the L-type calcium
current (ICaL) by 1.018 and INaL by 2.274. These values
were calculated by fitting to experimental data from
Computing in Cardiology 2016; VOL 43;
DOI:10.22489/CinC.2016.253-483

O’Hara et al. using a Genetic Algorithm-based
Parameterization for Systems Modeling [8]. Briefly, an
initial set of parameters are defined within a certain range
and their goodness of fit is assessed using an objectivefunction defined as the weighted sum of the squared
errors between model values and experimental
measurements. The set of parameters then undergoes
various changes (i.e. mutation and recombination) tocreate a new generation of parameters and this process is
continued until a global optimum is reached.
The experimental data used in the algorithm are takenfrom the ORd model paper [3] and shows APD ratedependence for control and 5 drug blocking conditions: 1
μM E-4031 (70% IKr block), 1 μM HMR-1556 (90% IKs
block), 1 μM nisoldipine (90% ICaL block), 100 μM
BaCl2 (90% IK1 block), 10 μM mexiletine (54% INaL, 9%
IKr, and 20% ICaL block). The simulated block was kept
the same as in the ORd paper, apart from mexiletine,
which was simulated using IC50 and hill coefficient datafrom Crumb et al. [8]. The algorithm was run using inhouse developed R scripts and parallel computing Snowand Rmpi packages on the FDA High Performance
Computer (HPC) with 160 cores.

3.

Results

3.1.

Fitting to experimental data

The modified ORd model was built by refitting to
APD rate dependence experimental data from O’Hara etal., as shown in Figure 1 and Table 1. The goodness of fitof each set of parameters generated by the genetic
algorithm was tested by calculating the sum of squares
error as in Table 1. The best parameters were passed onto
the next generation and the rest underwent mutation,
recombination, migration and repopulation to create the
new set of parameters. Here we present the bestparameters that showed the smallest error.
Under control conditions both the original andmodified models display a similar behavior, although
APD90 is shorter in the modified ORd. However, theoverall error under control conditions is smaller for themodified ORd (15.01) than the original ORd (17.20)
models (see Table 1). Furthermore, the modified ORdmodel shows a better fit to the experimental data for the
INaL, ICaL and IKr blockers (23.98 vs. 92.92; 0.75 vs. 5.29;
145.03 vs. 15.96) while the two models show similar
results for the IKs blocker (HMR-1556). Finally, themodified ORd shows good agreement with experimental
data for faster CLs ≤ 1000 ms for the IK1 blocker (BaCl2).
Therefore, overall the modified ORd displays a closer
match to experimental data (average error of 19.85 vs.
57.77). The main improvements are observed for the INaL
and IKr blockers, mexiletine and E-4031.

3.2.

AP and current traces of the models

As described in the methods all of the current
conductances are increased in the modified ORd model,
however, the AP shape of both models is very similar, as
shown in Figure 2. The smallest change in current
amplitude observed is ICaL, which only has a 1.8% change
in conductance. However, clear differences are observed
for all other currents (IKr, INaL, IKs and IK1) with thebiggest changes occurring for INaL (conductance isincreased by 227%) and IKs (conductance is increased by
165%). Therefore, INaL plays a bigger role in the modified
ORd model given there is a greater increase in INaL
compared to IKr.

Figure 1. Control, 10 μM mexiletine, 1 μM HMR-1556, 1
μM nisoldipine, 100 μM BACl2 and 1 μM E-4031 steadystate APD rate dependence for varying cycle lengths(CLs) for the original O’Hara et al. (ORd; dashed lines),
the modified ORd model (solid line) and experimental
data mean and standard deviation from O’Hara et al. [3](error bars). Control shows action potential duration

(APD) at 90% (APD90), 70% (APD70), 50% (APD50)
and 30% (APD30) repolarization. All other panels show
APD90.
Table 1. Sum of squares error between APD ratedependence experimental data mean and simulation
results (see Figure 1) for the original ORd and themodified ORd.
Sum of squares error

Original ORd

Control
10 μM mexiletine
1 μM HMR-1556
1 μM E-4031
100 μM BaCl2
1 μM nisoldipine

17.2
92.92
56.35
145.0329.83
5.29

Modified
ORd
15.01
23.98
43.54
15.96
15.96
0.75

Average ± SD

57.77 ± 52.98

19.85 ± 16.67

3.3.

Drug-induced APD prolongation

Block of INaL is underestimated and block of IKr isoverestimated in the original ORd model, as shown in
Figure 3. The modified ORd shows a smaller APD
prolongation for 75% IKr block compared to the original
ORd (192.5 ms vs 238.52 ms) and a greater decrease in
APD for 75% INaL block (42.51 ms vs. 16.5 ms). As
shown in Figure 1, this is closer to the experimental datawith E-4031, a potent IKr blocker, and mexiletine, a potent
INaL blocker. Proper characterization of INaL block in themodel is important as it plays an important role incounteracting pro-arrhythmic APD prolongation of IKr
block.

Figure 3. AP traces for 75% IKr (left panel) and INaL block(right panel); original ORd (dashed black line); modified
ORd (solid black line); control (gray).

Figure 4. Original ORd (left panel) and modified ORd(right panel) ΔAPD (%) with a CL of 500, 1000, 2000
and 4000 ms and 20X Cmax for a subset of the CiPA
drugs (high/intermediate risk in gray and low risk inblack).
Figure 2. Action potential (AP), L-type calcium current(ICaL), delayed rectifier potassium current (IKr), latesodium current (INaL), slow rectifier potassium current(IKs) and inwardly rectifying potassium current (IK1)
traces under control conditions for the original ORd(dashed line) and the modified ORd (solid line) for CLs
of 500 (dark gray), 1000 (black) and 2000 (light gray) ms.

The increased effect of INaL is demonstrated in Figure
4, which shows APDs for varying CLs in both models fora subset of the CiPA drugs. As expected for all models
the APD prolongation increases as the CL increases.
However, in the original ORd model ranolazine andmexiletine (INaL blockers) show an increase in APD
similar to other high and intermediate risk drugs. This is
improved in the modified ORd model, where the APD

We would like to thank Drs Jose Vicente, Robbert
Zusterzeel, Wendy Wu, Jiansong Sheng, Phu N. Tran and
Min Wu for their support and numerous discussions.

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References

Address for correspondence.

prolongation of the INaL blockers is closer to other safe
drugs such as verapamil (ICaL blocker) and is clearly
separated from higher risk drugs. Therefore, theseparation between the safe risk category and theintermediate and high risk categories is improved in themodified ORd model.

5.

Discussion

This study presents a modified ORd model with a new
set of scaling conductances for the IKr, INaL, ICaL, IKs and
IK1 currents that better fit APD rate dependence
experimental data, improve the prediction of INaL block on
APD and the separation of the low TdP risk CiPA drugs
from the intermediate and high risk CiPA drugs.
This study has certain limitations that should be taken
into account. Firstly, IC50 current blocking data for highCmax were extrapolated in some cases as theconcentrations simulated were higher than theexperimental concentrations tested by Crumb et al., as
was the case for the IKr blocking effect of mexiletine [7].
Secondly, APD, like QT, is not a predictive marker ofdrug-induced TdP risk [2]; therefore this metric ispresented to show the change in APD prolongationinduced by ICaL and INaL block rather than attempting toshow clear separation between the TdP risk categories.
The authors of this study are investigating other more
specific and sensitive metrics of TdP risk. Thirdly,
dynamics of the IKr current play an important role in therisk of TdP, particularly for high and intermediate risk
drugs [9], [10], and therefore, an updated ORd model iscurrently being developed based on our previously
published model [11]. Finally as shown in Figure 1,
despite the change in IKs, the original and modified ORdmodels show similar results due to changes in IKs having
little effect on APD, as shown previously [12], howeverunder certain conditions, such as beta-stimulation, effects
may be more pronounced.
In conclusion, this study presents important results forthe development of a modified ORd human ventricularaction potential model that can more accurately predict
drug-induced TdP, particularly in the presence of INaL
block.

Acknowledgements[1] G. A. Gintant, “Preclinical Torsades-de-Pointes Screens:
Advantages and limitations of surrogate and direct
approaches in evaluating proarrhythmic risk,” Pharmacol.
Ther., vol. 119, no. 2, pp. 199–209, Aug. 2008.

Sara Dutta
U.S. Food and Drug Administration, Building 64, Room 2084,
10903 New Hampshire Ave, Silver Spring, MD 20993.
sara.dutta@fda.hhs.gov.