Need for and feasibility of a
comprehensive non-clinical assay for the
pro-arrhythmic potential of new drugs
D Abernethy 1 , AM Brown 2 , T Colatsky 1 , C Garnett 3 , G Gintant 4 , CT January 5 , L
Johannesen 1 , J Koerner 1 , J Kramer 2 , N Kruhlak 1 , D Leishman 6 , M Malik 7 , S Polak 8 ,
P Sager 9 , N Stockbridge 1 , D Strauss 1 , N Thomas 10 , J Zhang 10
1
Food and Drug Administration; 2ChanTest ; 3Certara; 4AbbVie; 5University of Wisconsin; 6Eli Lilly; 7St.
Paul’s Hospital; 8SimCyp; 9Independent Consultant; 10GE
This is a working draft, upon which, at any given point in time, one cannot expect its authors to
agree on every particular.
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Introduction
Between 1991 and 2003, 6 drugs were removed from marketing in the US because of documented
evidence that patients developed QT interval prolongation on ECG leading to the life-threatening
ventricular arrhythmia torsade de pointes (TdP). This risk was not fully appreciated at the time of their
approval. The cellular mechanisms leading to TdP by these drugs are fairly well understood; most
commonly it results from blockade of a specific potassium channel responsible for the rapidly activating
delayed rectifier potassium current (IKr). IKr is responsible for the bulk of ventricular repolarization and is
coded by the human ether-a-go-go-related gene (hERG or KCNH2) gene.
International response to this crisis in drug development was rapid and incisive. ECG evidence of druginduced QT prolongation formed the basis of systematic monitoring of proarrhythmia risk of new drugs
as detailed in ICH E14. Over time, the in vitro measurement of a drug’s effect on the hERG current (ICH
S7B) routinely has become a gatekeeper within the pharmaceutical industry for drugs moving into
clinical development. By several criteria, these efforts have been quite successful. Since 2003, no drugs
have been removed from marketing because of risk of TdP, and the number of TdP cases reported for
drugs not intended to treat arrhythmias has clearly declined. None-the-less, drugs continue to be
associated with QT interval prolongation and a risk for proarrhythmia (see Azcert.org or qtdrugs.org).
While these statistics bespeak success, there are reasons to be skeptical, and even concerned, about the
overall impact of this strategy. Regrettably, the ion selectivity filter at the mouth of the hERG channel,
which faces the intracellular environment of the cardiac myocyte, is prone to bind organic entities that
are otherwise useful parts of drug molecules. While TdP is associated with prolongation of the heartrate corrected QT (QTc) interval on the ECG and that QTc prolongation is highly sensitive to drugs and
other conditions, it is a poorly specific marker for the development of TdP.
TdP is triggered by early afterdepolarizations and the proclivity of cardiac myocytes to undergo
depolarization during the repolarization phase of the action potential, plus heterogeneity of
repolarization. Prolongation of the QT interval alone cannot capture these abnormalities. Furthermore,
the QT interval may also be prolonged by multiple additional mechanisms (e.g., those related to central
nervous system and autonomic modulations) that are not directly related to a specific current and may
represent no proarrhythmic potential. There are indeed many drugs that can prolong the QT interval
without causing TdP (e.g., phenobarbital, ranolazine, and alfuzosin), even some that are potent IKr
blockers with effects on the QT interval prolongation but do not induce TdP. There is no mystery in this;
inhibition of inward currents of the cardiac myocyte reduce the potential for initiation of an arrhythmia
in the setting of hERG current inhibition.
Thus, using the hERG assay as a gatekeeper to avoid affecting the QT interval may be having a
deleterious effect on drug development by inhibiting the development of drugs whose clinical benefits
could well exceed the risk of arrhythmia.
Currently, a demonstrated effect of a drug candidate on hERG, even when it also affects inward
currents, can result in it not progressing into humans, despite its potential therapeutic benefits.
Likewise, the finding of even minor QTc prolongation in man can result in termination of a drug’s
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development, even if the risk of arrhythmia is very small. Additionally, the assessment of drug effects on
QTc are usually fairly late in development, and the drug development pathway would be more efficient
if risk of arrhythmia could be ascertained early in the non-clinical discovery stage.
New drug development is largely the product of intelligent engineering, leveraging what one knows
about structure-function relationships and high-throughput assays to design molecules that target
receptors of interest and avoid others. Much emphasis is now placed on avoidance of potential
interactions with hERG; in fact, from the field of inherited arrhythmia syndromes, the ability to induce
QT interval prolongation and TdP has been associated with multiple ion channels and channel regulatory
proteins. It is estimated that there are on the order of a thousand potential drug receptor targets; even
after accounting for structurally and evolutionarily related families of receptors, the parameter space is
large, and it is questionable how optimum is a strategy that reduces the risk along one axis while
incurring off-target effects elsewhere, possibly compromising the affinity for the target receptor as well.
How often development has stalled in particular therapeutic areas for want of a compound free from
hERG issues is not known, but anecdotally occurs.
We have asked whether one might be able to define a new paradigm for drug development that uses
high-throughput methods to develop a more comprehensive picture of a drug’s direct proarrhythmic
potential (as compared to effects on hERG current and consequently on the QT interval), reducing false
positive conclusions that exclude safe drugs from development, through a more nuanced understanding
of drug effects on ion channels of cardiac myocytes, while maintaining the presently achieved low rate
of false negative safety evaluations. In the sections that follow, we survey technologies that might be
and perhaps should be employed. In particular, we have reviewed technologies with the potential for
high throughput, to serve dual purposes of achieving small confidence intervals and allowing use of
some assay components to inform compound selection.
A more complete characterization of ion channel effects will also detect non-hERG arrhythmogenic
settings, for example where the risk is from isolated sodium channel block, but these cases are probably
not missed by current procedures.
We also recognize that employment of a more permissive paradigm may result in development and
approval of drugs whose true potential for pro-arrhythmic risk is not evident until post-marketing. We
assert our belief that a more comprehensive assessment of proarrhythmic risk will stimulate
development of new and useful drugs, a societal impact greater than that of the risk of rare product
removal for severe but very rare side effects.
We survey in the sections to follow technologies that were considered as possible contributors to a
comprehensive assessment of proarrhythmic risk.
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Available technologies
QSAR
Quantitative structure-activity relationship (QSAR) models describe the relationship between molecular
structural features and properties, and activity at a given toxicological or pharmacological endpoint. The
concept of QSARs was first reported by Hansch and Fujita in 1964 (Hansch and Fujita, 1964) describing
the relationship between combinations of physical and chemical properties, and endpoints such as
anesthetic potency and rodent carcinogenicity. Since that time, the approach has been expanded to a
vast array of endpoints ranging from in vitro mutagenicity to clinical cardiovascular effects, utilizing
thousands of different, though not necessarily independent, molecular descriptors. The early Corwin
Hansch models (often termed “Hansch equations”) were simple, mathematically-derived relationships
that described the activity of a small number of highly similar structures. Nowadays, a range of complex
mathematical algorithms are employed to derive such relationships using computational horsepower,
and have the capability to identify correlations across large datasets containing thousands of diverse
chemicals.
Evaluation of the proarrhythmic potential of an investigational compound at the in vitro level is heavily
based on assessing hERG channel blocking potency, which has led to the development of datasets for
this endpoint that have subsequently been utilized for QSAR modeling. The majority of these modeling
efforts utilize the IC50 value for training and prediction (Aronov et al., 2007), with a few notable
exceptions that model clinical (Ivanciuc, 2006; Yap et al., 2004) and post-market (Frid and Matthews,
2010) cases of arrhythmias. Commonly used methods for QSAR modeling include partial least squares
regression analysis, support vector machines, discriminant analysis, and artificial neural networks, in
combination with calculated and latent variable descriptor sets in both two and three dimensions.
Artificial neural networks are popular due to the advantages they offer in flexibility and predictivity, but
a major limitation is the lack of transparency in their predictions, which can be difficult to relate back to
observed phenomena and can essentially render the models black boxes. In contrast, models based on
regression algorithms, for example, can be easier to deconstruct and their predictions can often be
translated into chemical and biological terms. Recently, Broccatelli et al. (2012) successfully utilized a
consensus modeling approach employing three classification models based on different QSAR
algorithms and complementary descriptor sets. They further investigated the correlation between hERG
inhibition and in vivo metabolism with active transport mechanisms across the same dataset, leading to
the proposal of a hybrid, qualitative model that exploits the strengths of both QSAR modeling and
mechanistic interpretation.
Model descriptors can be categorized as those that are explicit in the structure, specifically substructural
fragments or functional groups, and those that are continuous numerical values, such as
electrotopological descriptors calculated for each structure using mathematical equations. While both
types of descriptors have been applied successfully to QSAR modeling of hERG blockade, achieving 7085% accuracy in validation studies (Aronov et al., 2007), interpretation of predictions generated by
models based on complex calculated descriptors can be problematic as they are harder to relate to
specific structurally alerting parts of molecules that can be readily interpreted from a chemistry
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perspective. In this regard, models constructed from more interpretable, fragment-based descriptors
can be more desirable if the goal is to utilize the predictions to modify or to discover new lead structures
in a series. Substructural “pharmacophores” that are generally considered to be associated with hERG
blockade include a basic nitrogen and a hydrophobic moiety such as an aromatic ring (Recanatini et al.,
2005; Doddareddy et al., 2010). The parameters of lipophilicity and molecular weight are also
recognized as important.
While raw datasets most often contain continuous IC50 values for channel inhibition, these values are
usually converted to dichotomous training/testing sets for classification modeling using a hard cutoff.
The selection of a cutoff can be subjective and can lead to difficulties in distinguishing among test
structures that have, for example, marginal IC50 values and those that have low values, with all being
equally characterized as positive. Various cutoff values of hERG channel IC50 have been reported, with
those for dichotomous datasets ranging from 0.130 and 40 μM (Aronov and Goldman, 2004; Sun, 2006;
O’Brien and de Groot, 2005; Fioravanzo et al., 2005; Buyck et al., 2002), the most commonly used
threshold being 1 μM (Keserü, 2003; Coi et al., 2006, Bains et al., 2004; Tobita et al., 2005). Doddareddy
et al. (2010) constructed three training sets with cutoff values for blockers of 3 μM, 6 μM, and 10 μM,
respectively, and a cutoff of 30 μM for non-blockers, effectively removing medium-potency blockers
from the dataset and thereby reducing noise, although potentially reducing the ability of the models to
predict drug structures with medium-blockade potency. Modifying the threshold without removing
drugs in this range generally has a direct impact on the predictive profile of the model, where a
conservatively low cutoff will lead to high specificity but lower sensitivity, and a high cutoff will result in
high sensitivity but low specificity.
QSAR models of pharmacologic or toxicologic activity can be categorized as those that exploit common
properties of the ligands only, often in two dimensions, or those that predict ligand-receptor
interactions, which can be achieved in cases where the receptor site has been structurally characterized.
The latter approach considers both the ligand and binding site(s) in three-dimensional conformations,
and accounts for the intramolecular proximity of ligand functional groups as well as their orientation
relative to amino acid residues in the binding site. Though labor intensive to develop and apply, ligandreceptor models can yield highly accurate results (Du et al., 2009). In situations where the binding site is
not structurally elucidated, homology models may be developed that make assumptions about the site
composition based on fully-characterized, biologically-related examples. Potassium channels have been
characterized by x-ray crystallography in both bacteria (Ruta et al., 2003) and mammals (Long et al.,
2005), and as such, many ligand-receptor modeling studies of hERG have been published based on the
homology assumption (Perry et al., 2010). However, this is not the case for sodium and calcium
channels; yet there are examples where either sodium or calcium current modification is implicated in
clinical activity (e.g., ranolazine, quinidine, moxifloxacin), suggesting that these channels warrant
investigation. This has led to limited exploration of QSAR models for calcium channels and, to a lesser
degree, sodium channel current inhibition. Because of the lack of crystal structures for these ion
channels, homology models have been adopted based on the closely-related KcsA channel in
combination with other biochemical information (Aronov et al., 2007). To our knowledge, there are no
reports of these homology models being used to screen for calcium or sodium channel inhibitors;
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however, there has been reported effort to generate ligand-only based QSAR models using in vitro
calcium (Cav1.2) channel IC50 values, which have been further combined with models for sodium
(Nav1.5) and potassium (Kv1.7) ion channels to improve the prediction of proarrhythmia potential over
the use of hERG models alone (Wiśniowska et al., 2012). Pharmacophoric features proposed from
selected studies include the requirement for multiple hydrophobic groups for potent sodium channel
inhibition (Zha et al., 2004), and a basic center such as a nitrogen in conjunction with a para-substituted
phenyl ring for calcium channel inhibition (Budriesi et al., 2005), although the limited scope of training
and testing data used in these analyses has the potential to limit their general applicability.
The application of QSAR models for hERG as part of a screening battery has been described by several
pharmaceutical companies (Seierstad and Agrafiotis, 2006; Gavaghan et al., 2007), where hERG QSAR
modeling in combination with a battery of physicochemical property screens, such as log D, polar
surface area, and amphiphilicity , as well as applying a filter for known hERG blockade pharmacophores,
is considered useful (Muller et al., 2007). It is suggested that the best time for hERG global QSAR
screening is early on, during lead identification, to reduce the size of combinatorial libraries, and then
subsequently to optimize leads using local QSARs in conjunction with newly generated in vitro data
(Muster et al., 2008). As new data are generated for a project, they can be fed back into the model to
improve its performance. Data variability in the published literature has been problematic when
constructing suitable model training sets, caused in part by differing cell lines, electrophysiological
parameters, and other experimental conditions, prompting the additional recommendation that
companies generate their own datasets using consistent protocols (Muster et al., 2008; Muller et al.,
2007).
FDA/CDER has developed a series of global QSAR models for arrhythmias, including QT-prolongation and
TdP, based on proportional reporting ratio scores for pooled preferred terms in post-market
spontaneous adverse event reports (Frid and Matthews, 2010). These models show reasonable
specificity and positive predictivity but limited sensitivity because of the relative lack of positive
examples in the training sets. From a classic regulatory standpoint, high sensitivity of an assay is
generally desirable, even to the detriment of specificity, to ensure that no safety signals are missed, but
as we have discussed, low specificity adversely impacts drug development globally. Low sensitivity in
QSAR models can often be corrected by supplementing a training set with more positive examples;
however, the nature of marketed drug data is that it is, not surprisingly, biased towards negative
examples. Overall, current models are not sufficiently predictive to use prospectively as the basis of
regulatory safety decisions, but may offer mechanistic insights through retrospective analysis, such as
during post-market review.
Receptor affinity assays
Receptor affinity assays have been developed for a number of voltage-gated ion channels, most notably
hERG, calcium, and sodium channels (see for example, Ragsdale et al., 1996; Hockerman et al., 1997;
Huang et al., 2010). Generally, these assays define the potential for drug-channel interactions based on
competitive binding/displacement to channel sites with membrane fragments from various sources
(typically non-cardiac or heterologous expression systems). Multiple binding sites for a channel may be
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known. For example, at least 7 binding sites are recognized for isoforms of the sodium channel, one of
which—batrachotoxin site 2—overlaps with the binding site of many small molecule inhibitors (Ragsdale
et al., 1996). While binding studies may prove useful for screening large compound libraries and early
hazard identification, the growing availability of automated patch clamp systems, heterologous systems
overexpressing specific channels, and recognition of the importance of state-dependent modulation of
drug effects has led to greater emphasis on functional assays that directly evaluate current block.
Single channel recording
Single ion channel currents can be recorded by applying gigaseal patch clamp to membrane patches
(Sakmann and Neher, 2009). The configuration of the patches can be cell-attached or excised and insideout or outside-out. The initial power of patch clamp lies in the ability to record function of a single
protein molecule in real time. Single channel current amplitudes (i) at constant membrane potentials
and gating represented by closed times, open times and burst durations are measured directly. Unitary
conductance and opening probability Po are calculated from the mean values. The macroscopic current
waveform can be predicted as Po × I, and, if the macroscopic current (I) is measured independently, the
number of functioning channels (N) can be calculated from I = NPo × i. On- and off-rates of drugs that
modulate single channels directly or allosterically can be calculated from changes in gating between
states usually as a function of drug concentration.
Single channel recording and data analysis are technically demanding and labor-intensive. The currents
may be picoamperes (pA) or less. In any case, seal resistances should be on the order of 10 GΩ. For subpA currents noise or fluctuation analysis may be required to calculate unitary conductances and gating.
Single channel studies are difficult to perform as they require significant amounts of data acquisition and
analysis. Single channels behave in a random manner and long recordings and averaging are required to
characterize their properties. Accordingly, single channel studies are rarely used in safety pharmacology
with only a few studies reported in the literature. Alfuzosin is a drug with no hERG blocking activity but
which has QT risk. Alfuzosin increases the peak current and the probability of late sodium channel
openings, burst duration and the number of openings per burst (Lacerda et al, 2008). The drug also
shortens the slow time constant for recovery from inactivation.
Because of the labor intensive and highly technical nature of the recording procedure, the potential of
single channel current recording for a role in a comprehensive proarrhythmia assay is low when
compared to the benefits of macroscopic current recording.
Single Macroscopic Currents
A variation of the patch clamp technique allows measuring macroscopic ion currents in small cells that
were refractory to standard methods of microelectrode voltage clamp. This whole-cell patch-clamp has
now become the customary application of the patch clamp technique, and from this work the following
picture has emerged.
The electrical activity in the whole heart that can be measured in the surface electrocardiogram (ECG) is
a result of propagated action potentials generated on a cellular level by conduction of Na+, Ca2+ and K+
ions through ion channel proteins. Conductance of sodium current (INa) into the cell through ion
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channel protein encoded by the Nav1.5 gene causes depolarization of the cardiomyocyte, the action
potential upstroke (Phase 0) and the QRS complex of the ECG. Following the upstroke of the action
potential, K+ current (Ito) through ion channel proteins encoded by the Kv4.3 gene results in the
formation of a notch in the action potential (Phase 1). The plateau of the action potential (Phase 2) and
S-T segment of the ECG is formed by inward Ca2+ current and late Na+ current through ion channels
encoded by Ca1.2 and Nav1.5 genes, respectively. Repolarization of the action potential (Phase 3) is a
result of outward K+ current (IKr, IKs) through ion channels encoded by hERG and KvLQT1/mink genes
(Kv1.5 in atria (IKur)) which brings the membrane potential back to resting levels (Phase 4). Outward K+
current (IK1) through channels encoded by the Kir2.1 gene are responsible for the resting potential in
cardiomyocytes. These macroscopic currents and their conductance timecourses are illustrated in Figure
1 below. There are additional currents in cardiomyocytes but the ones mentioned above have been the
most common targets used to identify arrhythmogenic risk.
Figure 1. Cardiac ion channel currents (Hoekstra et al., 2012)
Hardware considerations: Throughput with manual patch clamp can be between 10 and 20 satisfactory
data points per day. For automated patch clamp when the constraint of GΩ seal resistance is relaxed,
throughput can be as great as 10,000 data points per day. As a result of the greatly enhanced
throughput, macroscopic currents are far more suitable for the requirements of drug development than
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are single channel currents. Drug effects on individual currents can be correlated with exposure in the
clinic and therapeutic indices of safety for each current can be calculated. Cell lines expressing cardiac
ion channels provide robust assays since the protocols for electrophysiology and cellular biology are
amenable to validation and can be standardized among different laboratories.
Voltage control is important, especially for drugs whose interactions with ion channels are influenced by
voltage. The patch clamp method applied to macroscopic currents has the disadvantage that substantial
currents flow through the pipette tip and the voltage applied to the channels is reduced by the product
of current and access resistance. Series resistance compensation can minimize this effect in the case of
manual patch clamp and some mid-throughput automated patch clamp instruments. However, for some
high-throughput automated patch clamping platforms, series resistance compensation may not be
available and substantial currents may flow through the shunt resistance. As a result the sensitivity is
reduced.
As mentioned earlier, a drug’s interaction with ion channels may be dependent on voltage so platforms
that do not control voltage throughout the experimental period may have results that are hard to
interpret. Nonspecific binding of the drug to the solution delivery system may reduce the concentration
of the drug that is applied to the cells, thereby making it appear that a drug is less potent than it actually
is (Brimecombe et al.).
Because of the hydrophobic nature of the materials used in solution delivery systems and patch plates
of automated platforms, concentrations of hydrophobic compounds may be reduced when small
volumes are added to relatively large wells in patch plates. For manual patch clamp, the volume of
solutions is so large, concentrations of the bath solutions can be measured and adjustments in potency
made accordingly. For high throughput systems (HTS) on automated platforms, measurement of bath
concentrations is not possible, so limitations introduced by nonspecific binding of the drug have to be
considered when large compound libraries are being screened. If more accuracy is desired in HTS, a
platform should be chosen whose solution delivery system is composed of materials that have reduced
binding properties. With proper controls, signal windows can be determined and acceptance thresholds
set to minimize errors of the first and second kind (Zhang et al. 1999).
We should be mindful that drugs may also prevent trafficking of ion channel proteins to the cell
membrane and can be torsadogenic. Pentamidine which is a treatment for pneumocystis carinii
pneumonia, does not directly inhibit ion channel currents but prevents the hERG ion channel from
reaching the cell surface. As a result, pentamidine therapy is often accompanied by prolongation of the
QT interval and in some instances, by TdP (Wible et al. 2005).
Voltage Protocols: The voltage across the cell membrane determines the state or conformation (open,
closed or inactivated) of voltage gated ion channels, which cycle between these states during the cardiac
cycle. The voltage across the cell membrane also determines how deep in the pore a charged drug can
go. As a result, a drug’s binding site may be more or less available depending on the state of the
channel. For example, lidocaine, a class 1B antiarrhythmic, preferentially binds to the open inactivated
state of the sodium channel with fast onset and offset kinetics. Therefore, it has little or no effects at
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slower heart rates but displays greater effects at faster heart rates. The patch clamp gives one complete
control of voltage and allows one to manipulate the conformational states of the channel so that drug
effects can be completely evaluated. The following voltage protocols were validated at ChanTest Corp.
using an extensive drug set.
hERG: A standard protocol designed to detect the onset and block of hERG current consists of a 500 ms
prepulse to -40 mV (for leak subtraction), followed by a 2-second activating pulse to 40 mV followed by
a 2-second test pulse to -40 mV. The pulse pattern is repeated continuously at 10-s intervals from a
holding potential of -80 mV. Peak tail current is measured during the -40 mV test pulse.
hNav1.5: Local anesthetics typically show state-dependent block resulting from a greater affinity for the
open and inactivated states of the Nav channel versus the resting state. State-dependent block
augmentation results in a phasic, inhibitory component that dissipates following return to the resting
state (Sheets et al., 2003 and 2007). Tonic (i.e., resting state) and phasic components of block can be
measured by applying a voltage stimulus pattern consisting of two depolarizing test pulses. From a
holding potential of -80 mV, an initial hyperpolarizing conditioning pre-pulse (-120 mV amplitude, 200
ms duration; to remove inactivation) is followed immediately by the first depolarizing test pulse (-10 mV,
200 ms duration). This is followed by a brief (200 ms duration) return to the holding potential and then a
second depolarizing test pulse (-10 mV, 20 ms). The pulse pattern is repeated at 10 s intervals to reach a
steady-state block. Peak inward current amplitudes during both the first test pulse (tonic block) and
second test pulse (phasic block) are measured (Kuryshev, 2010). Drugs that preferentially bind to the
open or inactivated state of the channel will show greater block to the second pulse than to the first.
hCav1.2: Cyproheptadine, verapamil, diltiazem, nicardipine (Dohmoto, 2003) and nifedipine (Wible et al.
2008), all show significantly greater potency at a holding potential at -40 mV versus -80 mV. Therefore,
voltage protocols that use holding potentials at -40 mV that partially inactivate the channels also can be
used to reveal state-dependent block augmentation in Cav1.2. A typical voltage protocol consists of a
depolarizing test pulse (150 ms; 0 mV) at 5-s intervals from a -40 mV holding potential. Peak current is
measured during the step to 0 mV. An alternative protocol that paces at a higher rate (1 Hz) may be
used because Kang and Rampe (2004) reported that tolterodine, a potent blocker of hERG (IC50 = 17
nM) potassium channels that does not appear to be torsadogenic in the clinic, is a potent blocker of Ltype calcium channel current during repetitive stimulation at a frequency of 1 Hz (IC50 = 144 nM) but
much less potent at 0.1 Hz (IC50 = 1084 nM). Cav1.2 block augmentation may offset hERG inhibition,
and mitigate tolterodine’s torsadogenic risk.
hKir2.1: Extracellular spermine inhibits steady-state outward Kir2.1 currents in a voltage dependent
manner (Chang, 2013); therefore it is important to determine if drug block is voltage dependent. This
can be determined by using a ramp protocol where onset and steady state block of the current is
measured by hyperpolarizing to -110 mV (100 ms duration) followed by 1-second ramp from -110 mV to
+40 mV) repeated at 10-s intervals from a holding potential of -60 mV.
hKvLQT1/hmink: The onset and steady state block of hKvLQT1/hminK current is measured using a pulse
pattern with fixed amplitudes (depolarization 60 mV for 2 s and repolarization -40 mV for 0.5 s)
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repeated at 20-s intervals from a holding potential of -80 mV. Current amplitude is measured at the end
of the step to 60 mV.
Macroscopic Currents in Native Cells: Drug effects on these macroscopic currents can be characterized
using cardiomyocytes isolated from hearts or derived from human stem cells using patch clamp
techniques. The individual ion current types can be isolated using pharmacological techniques. For
example, IKs can be separated from IKr by inhibiting IKr using a selective blocker such as E-4031. IKr is a
very small current in native cells and difficult to measure without isolating it pharmacologically
(Sanguinetti and Jurkewicz). In other cases, individual current types may also be isolated using specific
voltage clamp techniques. For example, ICa may be isolated from INa by raising the membrane potential
to more depolarized potentials in order to inactivate INa.
Characterizing ionic currents from isolated myocytes is made challenging by the necessity to isolate each
current type pharmacologically and by the relatively small size of some of the currents of interest. These
complications create challenges for developing a high throughput assay.
Macroscopic Currents in Heterologous Expression Systems: Specific ion channel genes can be
expressed functionally in cell lines and macroscopic currents can be measured using manual or
automated patch clamp. In either case, seal resistances can be GΩs or less.
However, these cells solve the problems of isolating a single current type and achieving high signal-tonoise.
Isolated Cardiac Myocytes
Primary animal cardiomyocytes and tissues are acknowledged as being less than fully reliable predictors
of human cardiac drug liability, largely because of species-specific differences and technical issues
related to maintaining in culture normally functioning native cardiomyocytes. In addition, for
proarrhythmia testing, the mechanisms contributing to specific arrhythmias (e.g., triggered activity, reentry, etc.) and disease states may vary by species.
Thus, cells derived from humans and expressing a human phenotype are viewed as critical for making
mechanism-based decisions related to humans, and there are two general sources for such cells.
Primary cardiomyocytes derived from human donor tissues are limited in supply for large scale
proarrhythmia assays and do not currently represent a reasonable alternative assay system for
widespread adoption.
In contrast, cardiomyocytes differentiated from either human embryonic stem cells (hESC) or from
human induced pluripotent stem cells (hiPSC) represent emerging options for standard cell-based
proarrhythmia assays (see Table 1). Stem cell derived human cardiomyocytes are now being produced at
industrial scale and are available from a number of commercial sources in culture or cryopreserved in
quantities to match applications ranging from manual patch clamping to high-content analysis. Upon
recovery from cryopreserved stocks, isolated cardiomyocytes can form contractile monolayers or other
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tissue constructs that can be maintained in culture for extended periods. Within days of seeding, these
cells express the expected cardiac related transcription factors, structural proteins and ion channels
characteristic of authentic human cardiomyocytes (Babiarz et al, 2011, Ma et al, 2011, Peng et al, 2010).
Ideally, these candidate models would be the basis for a comprehensive integrated approach to
evaluate electrophysiological effects (using manual patch, automated patch, multi-electrode arrays and
impedance techniques) as well as structural, biochemical and bioenergetics effects using appropriate
analytical procedures (e.g., HCA imaging, biochemical and bioenergetics assays) to inform on potential
effects leading to cardiac dysfunction.
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Table 1 Comparative qualities of human cardiomyocytes for drug liability assessment
Isolated Primary
Human
Cardiomyocytes
Advantages
Disadvantages
 Closest direct equivalent of
 Limited supply
adult human heart
 Extensively used with
(availability and scale).
 Donor variability (age,
established methods for
health, drug treatment
isolation and assay
etc.)
 Variability in preparation
methods
 Limited compatibility
with wider range of
analytical techniques
Stem Cell (hESC/hiPS)
 Reproducible supply (clonal)
 Recent development with
derived Human
 Scalable production
limited historical data in
Cardiomyocytes
 Compatible with wide range of
drug liability assays
electrophysiological techniques
 Immature phenotype
(manual patch, automated
 Membrane potential more
patch, microelectrode array,
depolarized compared to
impedance)
adult ventricular
 Compatible with wide range of
myocytes
complementary analysis
methods (HCA imaging,
bioenergetics, biochemical
assays)
 Normal and disease models
Currently, hESC and hiPSC are renewable sources of cells that can be expanded and differentiated
(Mummery et al., 2012) on a large scale into cardiomyocytes possessing many of the phenotypic and
functional properties characteristic of native human cardiomyocytes (Zeevi-Levin N et al., 2010, Dreund
C et al., 2009). Stem cell derived cardiomyocytes are a relatively new introduction to the field of cardiac
liability evaluation and on-going discussions continue around the nature, phenotype, and maturity of
these cells as well as how these characteristics contribute to the robustness of a predictive model. Basic
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studies characterizing the electrophysiological properties of these cardiomyocytes have demonstrated
the presence of multiple ionic currents with similar current density and kinetic properties resembling
those found in adult cardiomyocytes (see Ma et al., 2011; supplemental Table 1; Hoekstra et al., 2012,
Table 2). However, differences in electrophysiological phenotypes have been reported that may be
related to batch variability, time in culture and culture techniques, and laboratory-dependent
experimental conditions. It should be noted that most hESC and hiPSC studies describe a heterogeneous
mixture of electrical phenotypes; variable proportions of nodal, atrial and ventricular subtypes are
present, typically with ventricular>atrial>>nodal abundance. This mixture of electrical phenotypes may
have implications for experimental sample size and biasing results when averaging over cell populations,
making comparisons across experiments more challenging. Automaticity, typically observed in low
density cultures of stem cell derived cardiomyocytes may require overdrive pacing when selected
stimulation rates are necessary for action potential/repolarization studies; such rapid pacing may limit
the ability to evaluate reverse rate-dependent effects of drugs, a characteristic of QT-prolonging drugs.
hESC and hiPSC cardiomyocytes are typically more depolarized compared to adult ventricular myocytes
(see Hoekstra et al., 2012; Table 1; see also Ma et al., 2011; Mummery et al., 2012 for discussion). This
electrophysiological phenotype may contribute to reduced upstroke velocities due to reduced sodium
current availability and may lead to an exaggerated effect of sodium (and also calcium) channel blockers
due to voltage-dependent block of both currents. Experimental efforts to create tissue specific
phenotypic and more mature cardiomyocyte models are likely to lead to further improvements (Lieu et
al., 2013).
It is perhaps inevitable that variations in assay protocols and conditions, including measurement
temperature, between laboratories in previously published voltage and current data from stem cell
derived cardiomyocytes (see for example Hoekstra et al., 2012 Table 2), as well as differences in stem
cell differentiation protocols, makes a precise and robust comparison between hESC and/or hiPSC
derived cardiomyocytes and native human cells difficult, and these problems exist even within the
reported literature for human cardiomyocytes. At present, cardiac stem cells are undergoing extensive
further characterization across a wide range of analytical platforms which will provide improved and
more standardized data for comparative purposes. The complexity and possible variances in expression
and activity of ion channels and other proteins and molecules influencing cardiomyocyte
electrophysiology coupled with the diversity of methods available for fully analyzing and characterizing
such complexity place high resource demands on such comparisons.
In any case it is recognized that these cell models must be subject to appropriate quality control
procedures and supplied by a process that ensures the key cardiomyocyte phenotypic characteristics
necessary for the performance of a diverse range of assays.
Despite the limitations cited above, electrophysiology studies conducted with cardiomyocytes derived
from hESC (Peng et al., 2010) and from hiPSC (reviewed in Hoekstra et al., 2012) have demonstrated
their basic utility in identifying and classifying clinically cardiotoxic drugs on the basis of
electrophysiological responses. In these studies stem cell derived cardiomyocytes were demonstrated to
show expected responses to test compounds of known pharmacology, producing alteration of action
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potential duration (APD) and induction of early afterdepolarizations (EADs) or delayed
afterdepolarizations (DADs)( Peng et al., 2010; Asai et al., 2010; Ma J et al., 2011), and these shared
quantitative similarity to EADs and DADs generated in native human cardiomyocytes. Comparison of
stem cell derived cardiomyocytes with standard ex-vivo canine, guinea pig, and rabbit Purkinje fiber
systems showed the cardiomyocytes to be of equal or increased sensitivity with respect to detection of
APD alterations. The overall electrophysiological and pharmacological similarities with human cardiac
myocytes coupled with continuity of supply and consideration of fitness of purpose across a wide range
of assays are key factors in evaluating the present and future role for these cells.
A number of automated patch clamping systems now provide the ability to record action potentials,
enabling repolarization studies in a high throughput format. The value of these assays may be somewhat
limited due to operation at ambient temperature; however instruments under development will have
the ability to run at physiological temperatures which may yield more informative data. Experiments
with multi-electrode arrays (MEA), which are routinely conducted at physiologic temperatures, offer the
potential to provide complementary information to cell patching. MEA analysis of beat rate, inter-spike
interval, field potential duration (informing on changes in action potential configuration and analogous
to repolarization/QT interval measures), T-wave amplitude and other metrics may be informative of
drug perturbation of cardiac function (Ma et al., 2011). As in automated patch clamping, continued
development of MEA systems is increasing the parallelization of measurement towards higher
throughput systems. Similarly, recent advances in measurements of physical movement and impedance
platforms enable high throughput measurements of a number of functional parameters characterizing
drug effects on cardiomyocyte contractility. However, it should be recognized that owing in part to their
non-anisotropic orientation and developmental status, the contractile properties and morphology of
stem cell derived cardiomyocyte tissue layers are not fully representative of isolated adult ventricular
myocytes.
A range of analytical approaches and platforms are now available that are potentially suitable for
deployment for analysis of drug effects on stem cell derived cardiomyocytes in a comprehensive
proarrhythmia analysis. As discussed above, standard manual patch clamping provides APD90, APD60,
EAD/DAD detection, APD triangulation and other standard measures to characterize drug effects on the
cardiac action potential that may be indicative and diagnostic of drug interaction with one or more ion
channels. It is important to note that MEA and impedance systems are compatible with measurements
made with cultured cells during extended drug exposures, providing a wider dose/time continuum than
is typical for patch clamp experiments. This ability may be important in detecting drug effects not
immediately manifest in short term exposure which may directly or indirectly promote arrhythmia, and
may prove advantageous to examine longer-term drug impact on other aspects of cardiomyocyte
structure and function (e.g., mitochondrial integrity, ionic homeostasis and bioenergetics) that may have
proarrhythmic activity (Jeong et al., 2012; Brown and O'Rourke, 2010). Such analyses would
complement imaging, biochemical and other assays for functional and structural cardiotoxicity using
stem cell derived cardiomyocytes (Matsa and Denning 2012; Mandenius et al., 2011 ) to provide
comprehensive surveillance of drug cardiac liability using a common in vitro model system.
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Given the limitations of predicting proarrhythmic liabilities of evolving compounds based solely on
assessing hERG current block in transfected cell lines (Gintant 2011), the ability to detect short- as well
as long-term pharmacologic effects, coupled with the ability to detect additional off-target
cardiotoxicity, human hESC and hiPSC represent a key element of a future comprehensive in vitro
proarrhythmia testing strategy.
In vitro and in vivo proarrhythmia assays
Numerous in vitro proarrhythmia models have been developed to evaluate proarrhythmic liabilities
using more integrated systems. One often-cited in vitro model, the female rabbit ventricular wedge
preparation, measures delayed repolarization assessed using either intracellular or extracellular action
potential recordings or extracellular field recordings of transmural electrical activity resembling ECGs.
This model, which is technically difficult to establish, returns a composite TdP score based upon weights
assigned to delayed ventricular repolarization (QT or APD prolongation), dispersion of repolarization
(the Tpeak-Tend/QT ratio), the incidence of early afterdepolarizations (with and without closely coupled
extrasystoles, as obtained from transmembrane potential recordings), and the development of TdP (Liu
et al., 2006, Chen et al., 2006). Another in vitro model, the Screenit Model, utilizes AV-blocked,
ventricle-paced, Langendorff-perfused female rabbit heart preparations to evaluate drug effects on
monophasic action potentials (recorded from epicardium and subendocardium) (Hondegehem et al.,
2001; Hondeghem and Hoffmann, 2003). Specifically, multiple effects on action potential repolarization
that contribute to a proarrhythmic substrate (Triangulation, Reverse use-dependence, Instability, and
Dispersion of repolarization, known with the acronym TRIaD) are evaluated in response to programmed
stimulation protocols. This automated assay, which has higher throughput than the wedge preparation,
was shown with a dataset of 55 drugs, to add value to an integrated QT/TdP risk assessment (in
particular with drugs having small margins between hERG IC50 values and predicted maximal
therapeutic free plasma drug concentrations) (Lawrence et al., 2006). Of potential concern is the ability
to calibrate the extent of proarrhythmic liability using this multiple-scored approach. Recent models
utilizing networks of cardiac myocytes on chips (Nomura et al., 2011) and cardiac stem cell monolayers
(Lee et al., 2012) to detect proarrhythmia in vitro, beyond measures of repolarization changes and
beating frequency, are evolving; these require further characterization, demonstration of
reproducibility, as well as assay sensitivity and specificity.
A number of in vivo proarrhythmia models have been described that utilize TdP as one evaluative
endpoint. In general, earlier versions of these models often used anesthetized rabbits and dogs in acute
studies, while later models have evolved that use conscious animals with electrically remodeled
ventricles (with reduced repolarization reserve to promote TdP) in chronic studies. In the chloraloseanesthetized rabbit model, alpha-1 adrenoceptor stimulation sensitizes animals to TdP and reduces the
doses necessary to cause TdP to those approaching those inducing TdP in conscious animals (Carlsson et
al., 1990, Carlsson, 2008). This model has been used to eliminate torsadogenic risk for novel drug
candidates targeting atrial fibrillation. However, it should be noted that drugs demonstrating alphaadrenergic blocking activity (thus counteracting the adrenergic action of methoxamine) as well as choice
of anesthetics may affect evaluation of proarrhythmic risk of specific compounds. Subsequently, dog
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(Vos et al., 1995; Vos et al., 1998; Eckhardt et al., 1998; Oros et al., 2008), rabbit (Tsuji et al., 2002),
microminipig (Sugiyama et al., 2011) and non-human primate (Satoh et al., 2006; Sugiyama, 2008)
models of chronic AV block have been used to study and screen for proarrhythmia related to delayed
repolarization (see Sugiyama, 2008, for an early review). With models of chronic AV block it appears that
beat-to-beat variability of repolarization (BVR) contributes to proarrhythmic (occurrence of TdP)
outcome (Thomsen et al. 2006), though irregular rhythms may be necessary to trigger TdP (REF). In
CAVB dogs, increases in BVR are associated with proarrhythmia, whereas antiarrhythmic treatment is
associated with a decrease in BVR (Thomsen et al., 2006, Antoons G et al., 2010). The chronic AV
blocked canine model has been employed to provide evidence against proarrhythmic liabilities with the
antibiotics moxifloxacin and azithromycin (Thomsen, 2006) and the antiarrhythmic ranolazine (Antoons
et al., 2010), suggesting assay specificity. It has been suggested that BVR may be superior to QT interval
prolongation in the prediction and prevention of drug-induced TdP (Thomsen, 2006). As compared to
the anesthetized methoxamine-sensitized rabbit model, the chronic AV blocked animal models are more
time- and resource intensive, and likely best employed during final compound selection or after
identification of concentration-dependent QT prolongation in later drug development (or humans) to
assess potential torsadogenic risk. This model is very low throughput, with relatively low numbers of
compounds tested; hence, assay sensitivity and specificity are less well characterized. The main
advantages of the AV blocked model include avoiding anesthetic effects through the use of conscious
animals, the repeated use of the same animals after washout periods, repeated dosing to detect
possible effects on channel trafficking, the ability to test metabolites and determine PK-PD profiles, and
model reproducibility. However, as with any model, and specifically one with a proarrhythmic endpoint,
it may be difficult to establish the safety margins needed for translation of effects to clinical studies
(Gintant, 2008).
A recent review has suggested BVR as a new biomarker for clinical proarrhythmia, speculating that STVQT measured clinically may be superior when compared with QT prolongation in assessing
proarrhythmic risk (Varkevisser et al., 2012), In addition, BVR could be used to identify individual
animals at risk of proarrhythmia, identifying patient populations at risk for ventricular arrhythmias or
monitoring safe exercise training programs for patients with cardiac disease (Nishi et al., 2011). As such,
the BVR approach used with preclinical in vivo proarrhythmia models could provide a more easily
translatable “bridge” between preclinical and clinical studies.
Models of Cardiac Myocytes
Computational models of cardiac myocytes describe the existing understanding of the heart’s physiology
at the cellular level. These models also form the foundation for larger and more complex systems, such
as three dimensional heart models describing the physiology at the whole organ level. Although
ventricular cell models have been developed and used predominantly to study cardiac arrhythmias, they
have also been utilized to assess alterations in cardiac activity due to drugs. In this section, we focus on
the general description of the cardiac myocyte model with a focus on the electrophysiology properties,
although other properties including contractility, metabolism and adrenergic stimulation are briefly
mentioned. We also provide examples of their use to further understanding of the electrophysiology
properties of a diverse set of drugs that inhibit hERG and other cardiac ion channels.
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The first mathematical description of the action potential initiation and propagation was formulated by
Alan Hodgkin and Andrew Huxley in 1952 (Hodgkin and Huxley 1952). The models were based on
experiments conducted in the giant squid axon which resulted in formulation of the Hodgkin–Huxley
paradigm [Equation 1]:
𝑑𝑉𝑚
𝑑𝑡
=−
∑𝑖 𝐼𝑖
𝐶𝑚
[Equation 1]
where Vm represents voltage at the membrane, Ii denotes all transmembrane ionic currents and Cm
describes cell capacitance. This paradigm has been applied to other excitable models including various
cardiac cells. In 1962, Noble and colleagues used this approach to describe the excitability of Purkinje
fibers which was then followed by the McAllister-Noble-Tsien (MNT) model (McAllister et al. 1975). The
MNT model is the first example of a “mosaic model” which combines the results from various
experiments of various ion channels to form a cardiac myocyte model. Other anatomic elements
described by the H-H type of model include sinoatrial nodes, atrial, and ventricular cells (Polak and
Fijorek 2012; Wilhelms et al. 2012).
The latest functional element of heart, namely the ventricular cells, is the most interesting model for
drug safety specialists because it can be used to evaluate primary or undesirable targets for single or
multiple drugs. The major achievements and milestones in the development of the electrophysiology of
the mammalian cardiac ventricular model can be listed as follows: Beeler-Reuter (Beeler and Reuter
1977), Luo-Rudy (Luo and Rudy 1991), Priebe-Beuckelmann (Priebe and Beuckelmann 1998),
tenTusscher (tenTusscher et al. 2004), Grandi-Bers (Grandi et al. 2010), and O’Hara (O’Hara et al. 2011)
models. These models differ in the number of included ionic currents and, more importantly, source of
the utilized data. The O’Hara model has been developed based exclusively on the human data obtained
from studies carried out on healthy cardiac myocytes. Common to all models in cardiac
electrophysiology, the lipid bilayer of the cellular membrane is modeled as a capacitor connected in
parallel with variable resistances and batteries representing the different ionic currents. In the majority
of cases the Nernst-Planck equation is used to describe the ion flux when diffusional and electric field
forces are present and the output is expressed in moles per cross-sectional area per unit time.
Markovian models, which describe various states of the channels and rates of the changes between
various states, allow for more detailed description of the complex interactions between drug and
channel in the situation when drug binds exclusively or preferably to the certain channel state (e.g.,
active, inactive, open, close)
The more recent ventricular myocyte models have included a description of calcium dynamics which is a
connector linking electrical activation and contractility (tenTusscher et al. 2004; Grandi et al. 2010;
O’Hara et al. 2011). The calcium dynamics and its subtle balance are maintained by the set of ionic
pumps, receptors and collecting spaces. In cooperation with other ions and their transporters, in a series
of processes and feedback loops, it contributes in a synchronous action of the cell sarcomers. Accurate
description of the calcium transport mechanisms in the contracting cell is a necessary element allowing
for the excitation-contraction coupling. Efforts have been taken to describe such phenomena
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mathematically resulting in models of healthy and diseased cells and tissues (Negroni and Lascano 2008;
Niederer and Smith 2008). From the drug development perspective, such functionality would be
necessary to simulate and investigate the electro-mechanical (E-M) window which is a time difference
between the end of electrical and mechanical systole (Niederer and Smith 2008). It has been suggested
that E-M window rather than QTc prolongation is a better surrogate for the TdP risk (Guns et al., 2012;
van der Linde et al., 2010). Scaling results from the animal models (e.g., guinea pigs, dogs) to the
humans makes the in silico based methods, based on the mathematical models of human physiology,
potentially very useful.
The above mentioned exemplar models describing the excitation-contraction coupling use simplified
approaches to illustrate the complex phenomena connected with the ATP-dependence of contraction.
To have more detailed insight, separate models have been developed where oxidative phosphorylation
processes including ATP metabolism together with the main kinases turnover were described (Matsuoka
et al. 2004a,b). Such models could be potentially useful for the analysis of a drug’s triggered cell
metabolism pathway disruption and prediction of cell viability (e.g., for the anticancer drugs). βadrenergic stimulation models offer further complexity to the computational approach and allow for
various virtual scenarios with drugs modifying adrenergic stimulation testing (Heijman et al. 2011).
The human ventricular heart wall is heterogeneous regarding the cell types and the transmural
differences between endocardial (endo), mid-myocardial (M), and epicardial (epi) cells should be
reflected accordingly in the in silico models (McAllister et al. 1975; Nabauer et al. 1996; Soltysinska et al.
2009; Szabo et al. 2005). The dynamics of most of the ion channels, e.g., in the sodium (INa), potassium
(IKr, IKs, IK1, Ito) and calcium (ICaL) currents, vary in different parts of the ventricular wall and these
differences need to be considered when predicting drug effects.
Survey of cardiac ion channel models: The main functional elements of human cardiomyocytes are the
channels, membrane pumps and signaling mechanisms that are responsible for the ion transportation
and subsequent ionic current maintenance. Their mathematical description is a crucial element of every
cardiac model based on the Hodgkin-Huxley paradigm. The models can be generalized into five types as
presented in Table 2. The number, type and source of currents in the mathematical model directly
influences its practical use in drug safety assessment and supports the need for a wider in vitro ionic
current screening.
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Table 2. Components of cardiac myocyte models
Current/Pump
Model
Beeler-
Luo-Rudy
Diffusion fluxes
Plateau and background
currents
Sarcoplasmic
currents
Exchanger
Membrane currents
Reuter
K+
Ito
K+
IKr
K+
IKs
K+
IK1
inward rectifier potassium current
Na+
INa
fast component of sodium current
Na+
INalate
late component of sodium current
Ca2+
ICaL
L-type calcium current
Na+/Ca2+
INaCa
Na+/K+
INaK
Ca2+
Jrel
Ca2+
Jleak
Ca2+
Jup
Ca2+
PriebeBeuckelmann
transient outward potassium current
rapid delayed rectifier potassium
current
slow delayed rectifier potassium
current
+ (as IK +
Ix)
+
TNNP
O’HaraRudy
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ (as IK)
+
+
+
+
+
Na+/Ca2+ exchanger current
+
+
+
+
Na+/K+ pump (ATPase) current
+
+
+
+
+
+
+
+
+
+
+
+
calcium uptake via SERCA pump
+
+
+
+
Jtr
calcium translocation from NSR to JSR
+
+
K+
IbK
background potassium current
K+
IpK
plateau potassium current
+
Na+
IbNa
background sodium current
+
+
+
+
Na+
IpNa
plateau sodium current
Ca2+
IbCa
background calcium current
+
+
+
+
Ca2+
IpCa
plateau calcium current
+
+
+
Na+
Jdiff,Na
K+
Jdiff,K
Ca2+
Jdiff,Ca
SR calcium release flux, via Ryanodine
receptor
leakage current from the SR to the
cytoplasm
+ (as Is)
+
+
+
diffusion flux between subspace (SS)
near the T-tubules and rest of
cardiomyocyte
+
+
SR – sarcoplasmic reticulum; NSR – network sarcoplasmic reticulum; JSR – junctional sarcoplasmic reticulum
Usability in the drug development process: Practical usability of the single cell models is limited by the
number of described physiological phenomena, availability of the input data and expected output. In
most cases, the drug-induced disruption in ionic transport is a point of interest and thus their virtual
modification is necessary to predict the compound-specific effects. It is then necessary to provide
information about the drug- and concentration-specific effect either measured by in vitro assays or
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predicted by QSAR models. Such data transferred to the model allows effects on the action potential to
be predicted across the full time course of drug exposure and across a wide range of heart rates and
other physiological parameters (Polak and Fijorek 2012; Polak et al. 2012).
Depending on the study advancement there are multiple places where the cell models and their
relatively simple combinations into one-dimensional (1D) strings can be used. They allow for primary
insight into the proarrhythmic potential at the early levels. Inter-individual variability prediction is
possible with use of the virtual population simulation (Fijorek et al. 2013; Polak and Fijorek 2012; Polak
et al. 2012). During the clinical stages more detailed and higher confidence risk prediction is enabled
when first in human pharmacokinetic data can be used to feed the system. Variability prediction,
including consideration of genetic risk factors, becomes the focal point of analysis.
Development and validation of the cardiac myocyte models are based on species-specific in vitro study
results. For the in silico models utilized for drug safety assessment, data of human origin are preferred.
Outputs can include the action potential and its derivatives (APD50/ APD90) up to the pseudo-ECG and its
derivatives (QTc, QRS). These outputs can then be compared with observed data to assess the model’s
accuracy and utility.
Examples of applications: Computational modeling of cardiac electrophysiology provides insight into
the mechanisms of drug-induced changes in cardiac behavior. Such an approach has been utilized since
the very first models of the cardiac cells appeared and activity of various drugs interacting with different
ionic channels were simulated.
Brennan and colleagues used computer simulations to explain the ionic mechanisms underlying sotalolinduced changes in T-wave morphology (Brennan et al., 2007). The interaction of sotalol with the hERG
channel was modeled using a Markov model for IKr incorporated in the cardiac myocyte model published
by ten Tusscher and Panfilov (ten Tusscher et al. 2004). This model was then incorporated into a
heterogeneous 1D fiber of human ventricular cells so that changes in T-wave morphology could be
simulated for varying drug concentrations. The simulation study showed dose-dependent prolongation
of the action potential duration, increased transmural dispersion of repolarization, and a decrease in
ECG T-wave amplitude. Such an effect is considered to be an indicator of pro-arrhythmogenesis.
Furthermore, the simulations conducted at different pacing rates showed the APD prolongation is larger
at slow heart rates than at fast heart-rates; thereby reproducing the reverse-rate dependence of hERG
inhibition observed experimentally.
Mirams and colleagues used the modified Grandi ventricular cardiac myocyte model to predict the
degree of drug-induced channel block changes for a variety of drugs, including verapramil (Mirams et al.
2011; O’Hara et al. 2011). Verapramil is known to have low risk of TdP, although it is a potent inhibitor
of hERG with an IC50 ranging from 94 to 500 nM (Fossa et al. 2004; Huang et al. 2010). The in silico
simulations showed that when only hERG block was considered, the APD was prolonged. However,
under conditions of multi-ion channel block with IKr, INa and ICaL, the APD was reduced. These simulations
underscore the importance of considering multiple ion channels in better predicting a drug’s effect on
ventricular repolarization.
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Obiol-Pardo and colleagues developed an in silico prediction of QT prolongation that couples 3D-QSAR
modeling to predict the binding affinity of the drug with the modified Luo-Rudy model for
cardiomyocytes (Luo and Rudy 1991; Polak 2013). These models were then used to build a simplified
model of the heart tissue based on the Hodgkin-Huxley formulation to simulate the propagation of a
cardiac beat. Model outputs were pseudo-ECG and APD from which QT interval prolongation and
transmural dispersion of repolarization were measured. This prediction system accurately described the
electrophysiological effects of four diverse drugs―dofetilide, ebastine, JNJ303 and HMR1556―in which
classic hERG-based models produced incorrect predictions.
Polak mimicked both arms of the clinical study pharmacokinetics (PK) and pharmacodynamics (PD) with
use of the in vitro - in vivo extrapolation approach and utilized simulated plasma drug concentration to
predict QTc and ΔQTc (Starmer and Grant 1985). Quinidine and its main metabolite 3-hydroxyquinidine
were used as the model compounds and the simulations were carried out at the population level where
virtual populations expressed the characteristics of the healthy volunteers group involved in the clinical
trials (Starmer et al. 1984).
Known limitations: The results of cell models can be no better than the data going into simulations. If
the kinetic properties of the channels are poorly resolved because of technical issues with the voltage
clamp data or there is poor resolution because of variability in response, these factors will limit the
predictions of which the simulations are capable. Drug effects have to be well characterized, including
state-dependent actions.
Computer Models of the Heart
To be most clinically useful, predictions of drug safety based on computational models of cardiac
electrical activity should ideally incorporate a consideration of the electrical and structural complexity of
the heart and how effects at the cellular level are manifested in the surface ECG. The initiation and
completion of each cardiac cycle is dependent on the coordinated interaction of different cell types
organized within a specific anatomical framework. The resulting sequence of impulse initiation and
propagation from atria to ventricles and through the myocardial wall produces the characteristic
waveforms seen in the surface electrocardiogram (ECG). Changes in the ECG waveform, such as QT
prolongation, can be diagnostic of disease as well as potentially important biomarkers for assessing drug
effects on the heart. However, because ECG morphology will be influenced by the quality of intercellular
coupling and the anatomical route for impulse propagation, the risk of triggering or sustaining a druginduced arrhythmia would not necessarily be predicted by a single cardiac myocyte model.
A computer model of the whole heart model should in principle be able to use information on the ion
channel behaviors observed in single cells to predict at least qualitatively the changes one would expect
to see in the surface ECG. Whole heart models can focus on impulse propagation alone (electrical
models) or the coupling of excitation and contraction (electromechanical models). Both may be useful in
the assessment of drug-induced cardiovascular risk (Trayanova 2011). Such simulations should also
serve to validate the cellular electrophysiological studies and provide a mechanistic basis for
understanding changes in the ECG, including altered T wave morphology. However, given the vast
amount of data and the number of assumptions that go into the development of a whole heart model,
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the utility of this approach will be strongly dependent on critical decisions regarding: (a) the complexity
of the component models, including whether the whole ventricle or only segments are represented and
how drug-channel interactions are described, and (b) the quality of the molecular and structural data
used to their assembly, but the specific requirements needed to have a truly predictive model have not
yet been established. Decisions on the complexity of the model will also determine the hardware
requirements and ultimately whether this approach can be considered a practical one in the evaluation
of cardiac safety.
Construction of a whole heart model: Each different type of cardiac cell within the heart has a unique
complement of ion channels, membrane transporters and signaling mechanisms that determine its
electrical characteristics, as well as a unique anatomic location and a specific electrical relationship with
its neighboring cells. Therefore, to simulate a normal ECG, a minimum of 2 types of models must be
used: (a) single cardiac cell models that simulate the cardiac action potential and impulse propagation,
and (b) a structural model (geometric or anatomic) that captures the structural features of the
pathway(s) for impulse propagation and defines how the single cell models are connected. An ECG
simulation should include a volume conductor (torso) model that represents the method used to
measure the ECG relative to the electrical axis of the heart within the torso. Finally, to simulate drug
effects, the model will need to consider drug-channel interactions and whether they will be represented
as static block based on IC50 values or a more dynamic use- or state-dependent behavior. A decision
must also be made about whether electro-mechanical coupling will be important and about the need to
include neurohumoral modulation and energy metabolism as part of the overall response.
The development of a partial or whole heart computer model begins with the assembly of an
appropriate set of individual biophysically detailed cardiac myocyte models that contain robust
mathematical descriptions of the various proteins, processes, and pathways needed to generate
electrical activity at the single cell level under normal physiological conditions. The different types of
cardiac myocyte models, their molecular content and their relative strengths and limitations are
discussed above. The single myocyte models can be modified to reflect experimentally determined
regional differences in cellular electrophysiology (e.g., endocardial, epicardial and mid-myocardial cell
action potentials), or calibrated against the generation on a target ECG waveform. The availability of
experimentally validated human cardiac cell models, however, is limited and there is currently no
consensus on which model is most correct for use in drug safety evaluation, or which set of electrical
behaviors must be reproduced to validate the model performance. Rather, the single cardiac cell models
used in human ECG simulations are likely to be derived from models developed for other species and
modified as needed to accommodate specific outcomes, e.g., the use of the Flaim-Giles-McCulloch
model of canine epicardial, endocardial and mid-myocardial cell responses to assess clinical risk in
patients with Long QT Syndrome (Hoefen et al, 2011).
The selected cell models are placed within a defined geometric architecture and the necessary electrical
connections made between cells. Structural models can attempt to represent the whole heart (most
often the ventricle) as an anatomically correct ‘virtual organ,’ or as pieces of the heart that correspond
to the types of isolated tissue preparations often used in the laboratory (e.g. Purkinje fiber-ventricular
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junction, ventricular wedge) (Muzikant and Penland, 2002; Benson et al. 2007; Myles et al. 2012; .
However, the simplest “whole heart models” are based on simplified geometries, including 1dimensional cables containing epicardial, endocardial and mid-myocardial cells that can simulate a
pseudo-transmural ECG (Hoefen et al., 2012), 2-dimensional models representing a section of the
ventricle (Virag et al., 1991; Bernus et al., 2002) or geometric shapes that resemble the ventricle in a
general way. More complex and realistic models take into account actual cardiac morphology and 3dimensional fiber orientation obtained either from detailed microscopic anatomic measurements (Smaill
et al. 2004) or based on patient-specific images (Vadakkumpadan et al. 2009). Each of these models can
be further modified to include the types of changes in myocardial structure that may occur due to
cardiac remodeling or infarction and scarring. In each case, simulation results can be compared with
actual nonclinical or clinical measurements for model calibration and validation.
In most case, the development of a 3-dimensional heart model will involve the use of a finite element
mesh for computation. When completed, the whole heart model is placed inside a volume conductor
surrounded by leads to generate a simulated ECG. While complex 3-dimensional models of the heart can
also be developed using simplified views of ionic current flow (e.g. Fitzhugh-Nagumo models, containing
a single inward and a single outward current; Berenfeld et al. 1999), these models lack the level of
pharmacologic detail needed to be useful in predicting drug-specific adverse effects. Whole heart
models have been used to explain various ECG changes (Linnenbank et al. 2010; Hoogendijk et al. 2009)
and to explore the mechanisms underlying arrhythmogenesis, including reentrant tachycardias,
ventricular fibrillation and their electrical and structural triggers (Ashihara et al., 2002; Hoefen et al.
2012; Moreno et al., 2012). The disruption of normal propagation pathways or the production of
repolarization heterogeneities by disease, drugs or electrical stimulation provide dynamic substrates for
the generation of serious arrhythmia that can be evaluated in silico using whole heart models.
The added value of using more complex whole heart models to support regulatory decisions on cardiac
safety have not yet been demonstrated, but there are a number of challenges that must be met,
including: a lack of data or consensus about the kinetic properties of ion channels and their regional
distribution across different species (including human); the need for robust and generally acceptable
validation data sets; transparency in the computational framework and the assumptions used to
develop, calibrate and run the model, and finally the resources needed to reduce multi-scale heart
modeling to practice. Quantitative measures that capture the degree of uncertainty present in model
predictions are also needed. Although there have been significant advances in computational power and
the efficiency of solver algorithms, the simulation of cardiac electrical activity using 3-dimensional heart
models remains computationally intensive, and published whole heart models have been reported to
contain on the order of 10 million to 100 million elements and to use more than 1 million cores (Mirin et
al. 2012). These issues may pose practical challenges in implementing this approach as a general means
of assessing cardiac safety.
Whole-heart models have been useful for gaining insight into mechanisms of arrhythmia. In particular,
regional heterogeneity of activity in the heart together with premature vulnerability to excitation during
the repolarization of the cardiac action potential are responsible for arrhythmias like TdP. Prediction of
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specific arrhythmias would require detailed knowledge of an individual’s heart structure and electrical
connectedness, not currently feasible. However, a whole-heart model with reasonable assumptions for
properties may be able to reproduce field potentials resembling the surface ECG, providing a means of
assessing whether the voltage clamp data reasonably capture a drug’s effects.
Divining channel effects from the human ECG
While most drugs that cause TdP block the hERG potassium channel and prolong QTc, multiple drugs
exist that block the hERG potassium channel and/or prolong QTc that do not increase the risk of TdP.
Three examples are amiodarone (Wu et al. 2008), ranolazine (Antzelevitch 2008), and verapamil (Zhang
et al. 1999). In addition to blocking the hERG potassium channel (outward current), these drugs also
block the late sodium current or the L-type calcium channel (inward currents). Thus, divining channel
effects from the human ECG could be valuable in distinguishing benign from malignant QT prolongation.
It may be possible to determine specific ion channel effects from the body-surface ECG by identifying
ECG patterns or signatures of specific ion channel block. This could involve analysis of T-wave
morphology, relative changes in sub-parts of the QT interval (i.e., Tpeak-to-Tend vs. J-to-Tpeak vs. QRS)
and changes in the PR interval. With regard to T-wave morphology, work to date has focused primarily
on characterizing the T-wave signature of sotalol, a potent hERG blocker (Couderc et al. 2008; Graff et
al. 2009). Sotalol not only prolongs QT, but causes flat, asymmetric and notched T waves (Graff et al.
2009). The flattening is thought to be proarrhythmic as it is a result of triangulation of the action
potential, which increases the time spent in the sodium and L-type calcium window where early after
depolarizations can occur (Shah and Hondeghem 2006).
While research with T-wave morphology is promising, only a limited number of drugs have been studied,
most of which only block the hERG channel. It may be possible to discern the effect of additional
channels by analyzing other parts of the ECG beyond ventricular repolarization. Verapamil is an example
of a drug that is a potent hERG blocker (Zhang et al. 1999), but has caused little-to-no TdP risk possibly
because of blocking the L-type calcium channel (Drew et al. 2010). The PR prolongation seen on the
body-surface ECG with verapamil is a sign of L-type calcium block, from ventricular depolarization in the
AV node being driven by the L-type calcium current (Johnston et al. 1981). Separately, detection of druginduced QRS prolongation strongly suggests the presence of sodium channel block, since ventricular
depolarization is mostly driven by the sodium current (Gintant et al. 2011).
In order for specific ion channel effects to be discerned from the human ECG, normal limits of day-today variability of the additional ECG measurements need to be established and more drugs with known
multi-channel effects both with and without TdP risk need to be studied. While in vitro ion channel
assays can be used to evaluate isolated channels, this approach is limited by not being able to detect the
effects of unknown metabolites and concomitant autonomic changes. Thus, detailed analysis of PQRST
intervals and morphology, beyond just the QT interval, may have an important role in a comprehensive
proarrhythmia assay.
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Desirable properties of a comprehensive proarrhythmia assay
The goal of a comprehensive assay is to provide a holistic risk assessment of drug induced cardiac
toxicity, and a basis to develop a logical risk management strategy if the drug candidate moves forward
to clinical use. In order to achieve such a goal, each component of the comprehensive assay needs to
provide an important aspect of the cardiac toxicity assessment. In addition, the contribution from each
assay should be complementary to help make the assessment predictive of proarrhythmic risk. It is
critical that the non-clinical tests be highly standardized to produce results reproducible across
laboratories.
Human cardiomyocytes play a critical role in this comprehensive assay through careful characterization
of ion channel effects. Further, combining with additional assessment of multiple cellular responses by
visualizing events at the sub-cellular level using high throughput automated microscopic analysis, human
cardiomyocytes can serve as a single unifying model system compatible with a range of analytical
methodologies throughout drug discovery and development.
In order to fulfill the potential as a useful tool in drug discovery and development, the desired human
cardiomyocytes are best equipped with the following properties:

Supplied at an industrial-scale source that can be maintained in culture for a minimum period of
time;

Consistency of the cells is maintained from batch to batch and within each batch

In each batch, appropriate proportion of cardiac cell subtype(s) required for the toxicity assay
(ventricular, atrial and nodal) are present;

Cells in each batch are well-characterized and verified to possess the phenotypic and functional
properties characteristic of authentic heart cells, for example

expressing expected cardiac related transcription factors, structural proteins and ion channels

expressing the major ion currents underlying the cardiac action potential
Proposed comprehensive in vitro proarrhythmia assay
Screening
A sponsor can always screen using any available technology and make its own decisions about what to
bring forward in development. Such strategies might involve some combination of QSAR, receptor
affinity, and the isolated hERG assay. However, no combination of these is currently accepted as a basis
for making regulatory decisions regarding proarrhythmic risk. In addition, a strategy that uses such
techniques is liable to conclude that a compound conveys proarrhythmic risk when a more thorough
assessment would reveal that it does not. A screening strategy based on a narrow range of mechanisms
is also prone to trade one liability for an unknown one.
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On the other hand, the ability to take a more definitive assay into the screening setting is very attractive,
and our consideration of alternatives fit for regulatory purposes has incorporated, as well as we could,
components amenable to higher throughput in vitro approaches.
Definitive assay
The overall strategy consists of two components, representing different levels of mechanistic
understanding and integration. The first component follows a reductionist approach, and consists of
assessment of drug effects on individual cardiac ionic currents. The second component focuses on
integration of drug effects on individual ionic currents to predict their overall net effect on the
ventricular electrical activity, either through reconstruction in silico or through experimental
approaches.
Source of channels: The key to a comprehensive in vitro proarrhythmia assay is the accurate and
reproducible characterization of a drug’s effect on various ion channel types found in human cardiac
myocytes. (Non-humans will not do.) To do this, one needs isolated cells expressing human channels,
and these systems fall into two categories—cloned cells over-expressing single human channel types
and human stem-cell-derived cardiac myocytes. The former have the advantage of large currents that
can be studied without effort to isolate them from other ionic currents. However, over-expressed
channels in these cells often do not have the full subunit structure of a native human ion channel, which
may result in different current kinetic and drug association/dissociation kinetics than seen with native
channels. While stem cell-derived cardiac myocytes likely express the full set of channel types, these
may not be ideal for studies of individual ionic currents since a) some channels may reflect a fetal
phenotype and b) it is may be difficult to isolate some ionic currents, particularly smaller currents
amongst larger currents that temporally overlap with channel activation or inactivation.
Many genetic variants are known for all ion channel types, and at some point it may prove important to
develop data on drug effects in some of these variants, either using heterologous systems
overexpressing the variant channel or in clonal hiPSC cardiomyocytes. However, our belief is that
assessment of effects in wild-type channels will provide better insight into proarrhythmia than has QT
studies in normal volunteers, and this is the place to start.
We propose a validation program that will resolve which sources of human cardiac ion channels are fit
for purpose.
Currents to study: What currents need to be considered? Clearly some are more important
determinants of the cardiac action potential than are others in terms of electrogenesis and known drugchannel interactions; those that contribute minimally to the surface ECG are probably of negligible
consequence. We propose to assess the final set of currents worth systematic evaluation in the course
of validation, but the list is certain to include those contributing significantly to both repolarization and
depolarization.
Assessment of drug effects: While functional drug-channel interactions have been characterized for
various channels across various cell lines, we believe it is necessary to do this again using a rigorously
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standardized protocol with replication to evaluate the extent of variability within cell batches, over the
lifetime of cells in culture, and across labs performing the work. This work will establish which cell
systems are fit for purpose and whether some combination of approaches will be necessary. Drug
effects are best evaluated at therapeutic and marginally supratherapeutic concentrations based on
anticipated Cmax values, inter-patient variability, and plasma protein binding; consideration should be
given to drug concentrations in the test chamber. Of particular interest are effects of major circulating
moieties.
What functional parameters to study for a particular current should be driven by the range of drug
effects that have been observed. These may include effects on peak conductance, changes to the
voltage- and time-dependent kinetics of the channels, and, where it is important, “use dependent” and
“voltage-dependent” modulation of current block. In practice, the assay must characterize the model’s
drug-dependent parameters well enough to constrain the predicted effects on the cardiac action
potential. When considering how drug effects are modeled, it should be recognized that one does not
want to miss possibly important pharmacological effects that might lead to arrhythmia, but too much
detail or incorporation of purely theoretical mechanisms will interfere with throughput and
interpretation of the data.
The replication that is required to characterize the models for various ionic currents in the first place and
to characterize the effects of drugs in practice will necessitate use of automated patch-clamp
techniques. Ideally, these should be performed with platforms providing giga-seal recordings.
Experimental considerations should include physiologic temperatures where possible, the as
temperature strongly affects channel kinetics and affects drug binding less strongly.
Most channel effects of drugs will be manifest with low latency, but effects on transcription or
trafficking have very long latencies that will necessitate pre-incubation before voltage clamp studies are
performed. It remains to be determined whether trafficking of other than hERG channels should be
considered.
Integration: In many cases, drugs may be shown to have little or no detectable effects on ion channels
at relevant exposure and there is no need for a step to examine or to predict the effects of the drug on
the cardiac action potential.
Integration in human ventricular cardiocytes: It is not possible to examine the effects on the action
potential in an intact heart, nor, currently, to do so reliably in tissues or cells obtained from human
hearts. Stem cell-derived human ventricular cardiocytes are available commercially from several
sources, or can be generated internally. While most ion channels in mature cardiac myocytes are
present in stem cell-derived human ventricular cardiocytes, the literature is conflicting regarding the
extent to which functional ion channel characteristics and current densities match those of myocytes
derived from the adult human heart. Such differences may reflect cells used, age or conditions of
culture, experimental conditions, etc. Thus, as argued for ionic current studies, a more rigorous
description of experimental protocols/conditions (or standardization) of protocols and techniques is
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needed to support the use of stem-cell derived cardiocytes as integrator of a comprehensive in vitro
proarrhythmia assay.
Integration in silico: Proper voltage clamp data and analysis permits reconstruction of the action
potential in silico, where needed changes in ion channel density, temperature, and perhaps other kinetic
properties can be applied, along with the drug-dependent effects, all taking into consideration the
variability in the cell model’s drug-dependent parameters. The cell model then allows one to predict
drug effects on the cardiac action potential as exposure levels rise and fall. While there is no universally
agreed-upon cellular model of arrhythmia, there are well described models of cellular electrogenesis,
some of which have been used to evaluate drugs affecting multiple cardiac ion channels.
It is also conceivable that differences in the electrophysiology of stem cell derived human cardiocytes vs.
mature ventricular myocytes may be overcome based on in silico approaches. This requires that deficits
in cardiocytes be first understood, and then “corrected” to predict effects on mature working
myocardium. Further refinement of presently available models of stem-cell derived cardiocytes will be
necessary.
Reconstruction of the propagating cardiac action potential in an in silico model of the whole heart is, as
previously stated, not a practical way to assess proarrhythmia, but we recommend that whole-heart
modeling be used in conjunction with the cell model to predict changes in the surface ECG in a person
administered drug. Comparison of the human ECG with the model would reveal whether the cell model
has missed some important effect.
Although models of the whole heart can give insight into mechanisms of proarrhythmia, the observation
is that humans only rarely have an arrhythmia even with potently arrhythmogenic agents. Thus, it is not
really practical to model the range of special states that render an individual prone to an arrhythmia at a
particular moment, and that is not necessary.
Most proarrhythmic vulnerability is impairment of repolarization that renders the cardiac myocyte
capable of supporting a depolarization much earlier than it should, and that vulnerability can be
assessed in the cell model. In the most vulnerable cases, the depolarization is fully manifest as an early
after-depolarization (EAD), but lesser degrees of vulnerability would show as sensitivity to exogenous
current stimuli. In this way, one can rank drugs on a scale that we believe straightforwardly relates to
proarrhythmic potential.
Substantial impairment of depolarization, especially sodium current block, is also proarrhythmic. We
believe that one can rank drugs based on this risk as well, by looking at effects on upstroke velocity.
Other in vivo assessment: What we are proposing addresses proarrhythmia stemming from direct or
indirect effects of drugs on cardiac ion channels, which is the most likely mechanism for the vast
majority of proarrhythmic effects. Other possibilities for adverse effects of drugs possibly would
manifest as arrhythmogenicity, including cytotoxicity, disruptions of cell coupling, interference with
excitation-contraction coupling, transmitter-mediated effects, and alterations in serum electrolytes. We
think that detection of such problems has been effective in studies of whole animals, and that the
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mechanisms can be elucidated in other assays, ranging from isolated cardiocytes to bits of tissue to
whole hearts or intact animals.
GENERAL COMPARISON: Proposed Comprehensive In Vitro ProArrhythmia Assay
ICH S7B
COMPREHENSIVE ProA ASSAY_
In vitro /in vivo components
In vitro component/in silico
Single cardiac current (hERG)/only IC50
Multiple currents/more granular, detailed
In vivo ECG provides integration
cellular effects
Integration provided at 2 levels (stem-cell derived
cardiocytes and in silico extensions)
Primary focus: delayed repolarization
Primary focus: proarrhythmia (ventricular
depolarization repolarization)
Follow-up studies (additional mechanisms)
More comprehensive, mechanism-based approach
Conclusions
Ensuring cardiac safety is essential for the successful development of novel therapeutic agents. Present
preclinical guidances describing in vitro (functional hERG current assessments) and in vivo (QT
prolongation) assays performed in early drug discovery have been partially successful in avoiding
subsequent QT prolongation assessed clinically (in thorough QT studies). It is likely that the emphasis on
hERG current and QT prolongation have had the unwanted effect of deprioritizing (or eliminating)
potentially efficacious compounds based on generally-assumed safety margins promulgated by the
industry and regulators. This has likely had a negative effect on the development of new efficacious
medications and made drug development less efficient. Further, it is well recognized (and also
demonstrated by growing body of preclinical-clinical concordance studies) that hERG is only one of
multiple ionic currents involved in delayed repolarization, and represents a good but imperfect
surrogate marker for proarrhythmia that is not highly specific. Importantly, the integrated
electrophysiologic response to a drug (from effects on multiple ionic currents; e.g., ranolazine,
verapamil, and phenobarbital) needs to be considered when evaluating proarrhythmic liabilities. Indeed,
hERG alone is insufficient and is overly sensitive by detecting false positive drug candidates. Additional
studies involving various experimental approaches and species may provide further mechanistic insights
on proarrhythmic drug effects. However, the interpretation of these results is often limited by unique
experimental conditions and species differences. Concern about potential confounding results by
species should be less problematic in the future with the advent of human stem-cell derived
cardiomyocytes.
This manuscript has presented and discussed the strengths and limitations of various preclinical in vitro
assays available that identify proarrhythmic hazard and calibrate proarrhythmic risk. Based on this
review, a new, proposal has emerged for a more comprehensive assessment of proarrhythmic risk that
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can replace the current approach of comprehensive QT assessment in humans using the thorough QT
study. One in vitro component utilizes higher throughput, automated planar-patch electrophysiological
approaches that enable the efficient evaluation of drug effects on multiple human cardiac currents
recorded from heterologous expression systems. An in silico model of human ventricular electrical
activity, utilizing the results of the drug effects on the in vitro cardiac currents, is also proposed to
integrate this comprehensive dataset, specifically focusing on the propensity for early
afterdepolarizations (associated with the initiation of TdP). Finally, a second in vitro component
evaluates drug-induced electrophysiologic effects on human stem cell-derived cardiomyocytes, allowing
for an integrated assessment of a drug’s effect based on biological integration using human-derived
myocytes. The focus would be on early afterdepolarizations and refractoriness to depolarization. This
multifaceted in vitro approach for proarrhythmic liabilities is based on best mechanistic understanding
of drug–induced proarrhythmia and human-derived biology, and rightly broadens the view of
proarrhythmic liabilities. Practically, this comprehensive non-clinical approach provides for the more
complete integrated risk assessment. Further, the proposal exploits novel (and yet evolving)
technologies and tissue/cellular engineering that was not possible when earlier regulatory guidelines
were being discussed nearly a decade ago.
It is well recognized that further contributions from industry, academics, and regulators are necessary to
define the strengths, limitations, and specific details of this new paradigm. The perspectives and insights
from all three groups are essential to advance drug safety and regulatory sciences based upon evolving
technologies, growing datasets comparing preclinical and clinical studies, and insights regarding basic
electrophysiologic mechanisms of proarrhythmia.
Identifying best practices for ion channel screening, in silico electrophysiologic modeling, and the use of
human stem-cell derived cardiac myocytes using a standardized approach (so that the results are not
laboratory-specific) to characterize electrophysiologic drug effects is essential to defining next steps
forward and benchmarking future process. To achieve this, areas of future work may include:



In Vitro Channel Assessment. A prospective, blinded evaluation of drug effects on ionic currents
conducted with HTS screening patch platforms using a selected set of compounds without and
with well-characterized clinical proarrhythmic effects. Multiple details will need to be resolved
while standardizing/optimizing this approach. Experimental conditions and voltage clamp
protocols will need to be refined and standardized, the extent of characterization of block
necessary (use- and voltage-dependence) determined, the number and types of cardiac
channels agreed upon, and variability and heterogeneity within (and across) platforms and
laboratories assessed,
In silico modeling. The choice of models, characteristics of channel block, as well as level of
model sensitivity and heterogeneity will need to be defined. Assessment of the results of
compounds that prolong the QT and do or do not cause proarrhythmia as well as negative
controls will need to be evaluated.
Human stem-cell derived cardiomyocytes. Likewise, efforts to determine the direct
electrophysiological effects of the same compounds on human stem-cell derived
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cardiomyocytes should be performed. Details to consider for such studies will include
experimental conditions, cell types, level of maturation, electrophysiologic phenotype and
stability, experimental protocols, reproducibility across laboratories, and overall assay
sensitivity. Results from the ion currents/in silico modeling approach and stem-cell derived
cardiomyocyte approach would be compared.
This new initiative and the outlined approaches have the real potential to move beyond using hERG and
QT assessments as proarrhythmic gatekeepers, thereby inhibiting the discovery and clinical
development of efficacious medications that would otherwise be discarded based on low specificity
safety assessments. This present proposal will guide the development of the next-generation
comprehensive preclinical assay to assess the proarrhythmic potential of evolving drugs. This will reduce
the early termination of drugs based solely on a hERG effect, make drug development more efficient by
largely determining proarrhythmia risk prior to human testing (and thus improving candidate selection),
and remove the current emphasis on burdensome and occasionally erroneous human QT testing to
assess drug-induced arrhythmic potential.
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