Comprehensive In Vitro Proarrhythmia
Assay (CiPA) Update from a Cardiac
Safety Research Consortium / Healthand Environmental Sciences Institute /FDA Meeting

Therapeutic Innovation& Regulatory Science
1-7ª The Author(s) 2018
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/2168479018795117
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David G. Strauss, MD, PhD1 , Gary Gintant, PhD2, Zhihua Li, PhD3,
Wendy Wu, PhD3, Ksenia Blinova, PhD3, Jose Vicente, PhD3 ,J. Rick Turner, PhD, DSc4, and Philip T. Sager, MD5

Abstract
A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting wasconvened in Washington, DC, on May 21, 2018, to bring together scientists, clinicians, and regulators from multiple geographic
regions to evaluate progress to date in the Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative, a new paradigm toevaluate proarrhythmic risk. Study reports from the 4 different components of the CiPA paradigm (ionic current studies, in silico
modeling to generate a Torsade Metric Score, human induced pluripotent stem cell–derived ventricular cardiomyocytes, andclinical ECG assessments including J-Tpeakc) were presented and discussed. This paper provides a high-level summary of the CiPA
data presented at the meeting.
Keywordsbalanced ion channel-blocking drugs, CiPA, human induced pluripotent stem cell-derived ventricular cardiomyocytes, J-Tpeakc,
torsade de pointes, torsade metric score

Introduction
Early public scientific discussions of the Comprehensive In
Vitro Proarrhythmia Assay (CiPA) Initiative were held at a
Think Tank Meeting convened at the US Food and Drug
Administration (FDA) headquarters on July 23, 2013, andsponsored by the FDA, the Health and Environmental Sciences
Institute (HESI), and the Cardiac Safety Research Consortium(CSRC).1 The goals of that Think Tank were to suggest components of CiPA, a new paradigm for assessing proarrhythmic
risk, facilitate transparent stakeholder input and discussions,
propose potential member organizations of a collaborative program to develop the specifics that would be needed, and consider first pragmatic steps. Since then, many international
collaborators on the CiPA Initiative have continued to workon its individual components, and productive discourse has
occurred at multiple meetings, including those cosponsoredby FDA, CSRC, HESI, and the Safety Pharmacology Society(SPS),2-4 dedicated sessions at the SPS Annual Meeting for thelast 3 years, and a 2017 FDA Advisory Committee meeting.5
To bring together scientists, clinicians, and regulators frommultiple geographic regions to evaluate progress to date,a CSRC/HESI/FDA-sponsored Think Tank Meeting wasconvened in Washington, DC, on May 21, 2018. This paper
provides a high-level report of the presented results.

Background
While the International Council on Harmonisation (ICH) S7B/
E14 regulatory landscape has successfully prevented new drugs
with an unrecognized propensity to induce torsade de pointes(Torsade) arrhythmia from entering the market, important
unanticipated limitations have become increasingly clear. The
1
Division of Applied Regulatory Science, Center for Drug Evaluation andResearch, US Food and Drug Administration, Silver Spring, MD, USA
2
AbbVie, Chicago, IL, USA
3
US Food and Drug Administration, Silver Spring, MD, USA
4
DRT Strategies, Inc, Arlington, VA, USA
5
Stanford University, Palo Alto, CA, USA

Submitted 9-Jul-2018; accepted 18-Jul-2018
Corresponding Author:
David G. Strauss, MD, PhD, Director, Division of Applied Regulatory Science,Center for Drug Evaluation and Research, US Food and Drug Administration,
Silver Spring, MD, USA.
Email: David.Strauss@fda.hhs.gov

2current landscape focuses largely on 2 surrogate measures that
do not have high positive predictive value in identifying torsadogenic drugs—a single outward cardiac potassium repolarizing ionic current (IKr, flowing through potassium channels
encoded by the human ether-à-go-go-related gene [hERG], andreferred to as the hERG current throughout this paper) and asingle associated electrocardiogram (ECG) characteristic, the
QT/QTc interval—rather than on direct evaluation of proarrhythmic risk. This focus has influenced whether drug candidates progress into human testing, since up to 70% of suchcandidates can exert an influence on hERG6,7 and are often
terminated during nonclinical development programs, andwhether clinical development proceeds past early-phase clinical
trials if a human QTc effect is demonstrated. Additionally,
delays in New Drug Application filing and/or approval of drugs
with an otherwise favorable safety profile and a demonstrated
therapeutic benefit may have occurred. In contrast, the CiPA
approach, which is predicated on a deep mechanistic understanding of the factors that cause Torsade, represents a paradigm shift
involving strategies that have the potential to improve specificity
in the early detection of genuine torsadogenic proarrhythmic
risks by focusing on a drug’s actual propensity to cause Torsade.
Drug-induced ventricular repolarization and risk of
Torsade are not due solely to hERG block. Rather, they are
dependent on a “balance” of multiple inward and outward
ionic currents active during the ventricular action potentialthat together define ventricular repolarization.4 With regard
to drug-induced Torsade, the most important currents in addition to hERG are 2 inward depolarizing currents, the latecomponent of the sodium current (flowing through channels
encoded by the Nav1.5 sodium channel gene, and referred toas late sodium current throughout this paper) and the L-type
calcium current (flowing through channels encoded by the
Cav1.2 calcium channel gene, and referred to as calcium current throughout this paper). Drugs that also reduce late sodium
and/or calcium currents at approximately equipotent concentrations to hERG are said to have balanced effects, resulting ina low risk of Torsade. Such drugs are referred to as balanced
ion channel–blocking drugs.8
Collectively, the 4 components of CiPA seek to characterizemore clearly the torsadogenic risk of drugs by providing a morecomprehensive assessment of a drug’s effect on multiple cardiac ionic currents using human-derived models. The first component is the in vitro assessment of drug-induced effects on
multiple ionic currents, focusing on 3 dominant plateau currents, namely hERG, late sodium, and calcium currents. The
second is in silico computer modeling, in which individual ion
channel data are integrated together in a model of the humanventricular myocyte with the goal of predicting clinical risk of
Torsade. This modeling yields a Torsade Metric Score, termed
qNET. When calibrated with clinically used drugs categorized
into high, intermediate, and low Torsade risk categories, qNET
provides a reliable measure of proarrhythmic risk. A third component, in vitro drug effects on human induced pluripotent stem
cell–derived ventricular cardiomyocytes (hiPSC-CMs), canTherapeutic Innovation & Regulatory Science XX(X)

Figure 1. Stylistic representation of QT, J-Tpeak, and Tpeak-Tend.
(J-Tpeakc, heart rate–corrected J-Tpeak interval; Tpeak-Tend, thetime between the peak of the T-wave and the end of the T-wave.)also be optionally employed to check for unanticipated effects
compared with nonclinical ion channel data and in silico modeling predictions. The fourth component, assessments madeduring phase 1 clinical trials, plays an important role in determining if there are unexpected ion channel effects manifestingon the human surface ECG compared with nonclinical ion
channel data, perhaps because of a human-specific metabolite
or differences in predicted protein binding or tissue concentration (eg, leading to unexpected QTc prolongation).
In this paradigm, therefore, risk of Torsade will be predicted
via in silico modeling of ion channel data, and an ECG biomarker(s) will be used to determine whether there are unexpected ion channel effects in humans. This requires an ECG
biomarker that is capable of discriminating between multichannel block with predominant hERG block and balanced ionchannel block. The heart rate–corrected J-Tpeak interval (JTpeakc), the time between the end of the QRS interval and thepeak of the T-wave, has been proposed as such a biomarker.
Figure 1 presents a stylistic representation of QT, J-Tpeak, andTpeak-Tend. Drugs that predominantly block hERG prolong
QTc by prolonging both J-Tpeakc (early repolarization) andTpeak-Tend (the time between the peak of the T-wave and theend of the T-wave, which represents late repolarization). In
contrast, balanced ion channel–blocking drugs involving hERG
along with late sodium and/or calcium prolong QTc by
prolonging Tpeak-Tend without prolonging J-Tpeakc. The
absence of significant J-Tpeakc prolongation in the presenceof QTc prolongation is therefore thought to be an “ECG signature” of a balanced ion channel–blocking drug.4
It is important to note that the ECG assessment is not being
performed in isolation, but rather as a part of an integrated risk
assessment with the nonclinical data. The assessment of proarrhythmic risk comes from the in silico Torsade Metric Score
qNET. As proposed at the meeting, in the presence of a modest
QTc prolonging drug (eg, 10- to 20-millisecond [ms] prolongation at highest therapeutic concentrations), a Torsade Metric
Score consistent with low proarrhythmic risk and an absence of
meaningful J-Tpeakc prolongation suggests a reduced need for
ECG monitoring in phase 3 clinical trials, and can also informlabeling. For example, in the future a drug label might state thatwhile a drug prolongs QTc by 15 ms, its integrated risk assessment suggests it is not associated with Torsade.

Strauss et al

3

Figure 2. Stylistic representation of CiPA components and early-phase ECG assessment. (CiPA, Comprehensive In Vitro Proarrhythmia Assay;
ECG, electrocardiogram.) Adapted from Figure 3, Vicente and colleagues.8

It should also be emphasized that the CiPA paradigm allows
sponsors the flexibility to employ individual components of the
CiPA component core set however they wish, incorporating
information obtained into the compound’s overall integrated
cardiac safety risk assessment. This flexibility includes the
freedom to choose to employ none of the components, although
this choice would hamper the ability to define proarrhythmic
risk and lead to a reliance on prior regulatory pathways. To use
CiPA for regulatory decision making, certain aspects will be
required. This notion of flexibility is itself an important new
development discussed at the Think Tank Meeting, independent of results presented for each of the CiPA components.
The multiple components of the CiPA paradigm are represented in Figure 2.

Results Presented at the Think Tank
The nonclinical data presented at the Think Tank involved
studying a set of 28 “CiPA Drugs.” These 28 gold standard
compounds were chosen to represent a varied spectrum of multiple electrophysiological mechanisms, including multichannel
blocking drugs. Each drug was ranked on clinically demonstrated risk of Torsade—high, intermediate, or low—basedon published reports, FDA’s Adverse Events Reporting System
database, other published data sources, and the opinion ofexpert clinical electrophysiologists and safety specialists. Two
subsets were then created: 12 training drugs (4 high Torsade
risk, 4 intermediate risk, and 4 low risk) to be used in in silico
model development and optimization and 16 validation drugs

Table 1. CiPA Model Training and Validation Drugs.
Risk Level for Torsade

Model Training

Model Validation

High

BepridilDofetilide
Quinidine
Sotalol
Chlorpromazine
Cisapride
Terfenadine
Ondansetron

AzimilideIbutilide
Vandetanib
Disopyramide
Astemizole
Clarithromycin
Clozapine
Domperidone
Droperidol
Pimozide
Risperidone
Loratadine
Metoprolol
Nifedipine
Nitrendipine
Tamoxifen

Intermediate

Low

Diltiazem
Mexiletine
Ranolazine
Verapamil

Abbreviation: CiPA, Comprehensive In Vitro Proarrhythmia Assay.

(4 high Torsade risk, 7 intermediate risk, and 5 low risk) to be
used in model validation (see Table 1).

In silico Modeling Approach and Validation Results
The patch clamp data that are obtained on human cardiac ionchannels are used as inputs for an in silico model of the humanventricular myocyte. Simulations are run, and a metric thatclassifies a drug’s level of risk for Torsade is calculated.

4

Therapeutic Innovation & Regulatory Science XX(X)Biophysical electrophysiology models were initially applied
to cardiac cells approximately 60 years ago, when Denis Noble
applied the Hodgkin & Huxley model to cardiac cells. This
represented the first quantitative in silico reconstruction ofcardiomyocyte excitability. These models described the dependence of currents on membrane voltage and on time, and how
cardiac electrical activity emerged from the interaction of multiple currents. For the purposes of CiPA, an open-source, electrophysiological model based on recordings of humanventricular tissue and cell data9 was enhanced and validated
for proarrhythmic risk prediction as follows:

Employ ionic current study results from a subset of 12
CiPA drugs (representing high, intermediate, and low
Torsade risk) for model training, optimization, andmetric development.10-12
Freeze the optimized model, CiPAORdv1.0 (including
risk classification thresholds), for subsequent validation.
Predict the proarrhythmic risk of the remaining 16 CiPA
drugs based on qNET scores, and assess the ability tocorrectly categorize the drugs into high, intermediate,
and low Torsade risk categories using predefined performance measures.

The validation process was performed for 2 different ioniccurrent data sets, each including dynamic kinetic data for drug
block of hERG current as well as simpler estimates of blockpotency for calcium and late sodium currents. The first data set
contained manual patch clamp data for all 3 currents (obtained
at 37 C), while the second “hybrid” data set consisted ofmanual hERG current data along with automated patch clamp
measurement of calcium and late sodium currents (the latter 2
obtained at room temperature).
Torsade Metric Score performance was evaluated in 4 different ways, with the first 2 measuring the performance ofranking validation drugs according to Torsade risk levels andthe last 2 evaluating the performance of classifying validation
drugs using predefined classification thresholds. The first evaluation employed receiver operating characteristic (ROC)
curves, that is, curves of sensitivity plotted against [1 – specificity]. In the present context, the area under the curve (AUC)of an ROC curve is the probability of correctly ranking ahigher-risk drug above a lower-risk drug. Two analyses wereconducted: one for low-risk versus high-or-intermediate risk
drugs and one for low-or-intermediate versus high-risk drugs.
Results generally fell in a range with AUC 0.9 for both themanual patch clamp and hybrid data sets, which was in thepredetermined Excellent range.
The second performance evaluation utilized rank performance using pairwise comparisons. The model is used to predict pairwise ranking for each drug pair included, comparing
the results to known ranking of risk of Torsade. “Correct
prediction fraction” across the 211 pairs indicates ranking performance across all 3 categories. This is then repeated 10,000
times through random sampling, thereby estimating theconfidence interval of the correct prediction fraction. Results
again fell in the Excellent range.
The third and fourth performance measures evaluated classification accuracy. The third measure employed likelihood
ratios (LRs), with LR positive (sensitivity/[1 – specificity])
indicating how much more likely a higher-risk drug will beclassified into the higher risk category than a lower-risk drug,
while inverse of LR negative (specificity/[1 – sensitivity]) indicated how much less likely a high-risk drug will be classified
into the lower risk category than a lower-risk drug. Here, thesensitivity and specificity were calculated based on specificclassification thresholds predetermined by the training data,
and the performance on 2 thresholds were evaluated separately:
one separating low-risk from high- or intermediate-risk drugs
and one separating high- from low- or intermediate-risk drugs.
The fourth performance measure calculates mean classification error for all validation drugs across 3 risk categories
using both classification thresholds, thus giving an assessmentof the overall classification performance. Over the 2 data sets
and the 2 classification performance measures, the results
were all above the predefined acceptable level, with 80%
falling in the excellent or good range (5/10 times excellent
and 3/10 times good).

Ion Channel Assays and Data
The flexibility of the in silico model and Torsade Metric Score
was demonstrated based on training and validating the modelwith 2 patch clamp data sets for the 12 training and 16 validation CiPA drugs. The model achieved high performance acrossboth data sets, demonstrating flexibility and robustness. However, there were notable differences in IC50 values between the
2 data sets for the late sodium and calcium currents. To minimize the need for every lab to run the full set of CiPA training
drugs in the future to calibrate the model, the following topics
and recommendations for ion channel experiments under CiPA
were presented:
1.

how the data with hERG, calcium, and late sodium
currents were generated and used to train and validate
the model;
2. how differences in experimental procedures for calcium
and late sodium lead to data variability; and3. how ion channel pharmacology can be standardized
under CiPA.
For hERG, the importance of using a long (10-second) depolarizing pulse to 0 mV to define kinetics of hERG block at
physiologic temperatures (37 C) was emphasized as necessary
to differentiate drugs into high, intermediate, and low proarrhythmic risk in subsequent in silico evaluations/reconstructions. Attention to hERG stability, background/leak current,
and general recording quality was emphasized to ensurehigh-quality recordings. For calcium studies, standardizationof charge carrier used (Ca2þ vs Ba2þ), region of analysis, temperature, and current stability prior to drug application wereStrauss et alshown to affect IC50 values. For late sodium studies, choice ofagonist to induce late sodium current (ATX-II vs veratridine),time required to achieve stable current recordings (“run-up” ofcurrent as agonists’ effects are activity-dependent), and regionof analysis for drug effect on late sodium current were shown toaffect the potency of drug block.
With standardized quality criteria, sponsors would be able todemonstrate data quality by submitting digital ion channel testing waveform data to regulatory agencies (as done with the
ECG Warehouse for clinical QT studies) and perform a limited
number of positive controls to demonstrate that results are notdifferent from data used to train the model.

Human Induced Pluripotent Stem
Cell–Derived Ventricular Cardiomyocytes
The role of hiPSC-CMs under CiPA is to provide an option tocheck for missed or unanticipated effects, although they may
play additional roles early in drug development. In drug discovery or early development, hiPSC-CMs can be used in theearly assessment of electrophysiological drug effects over awide range of exposures. Later in development, hiPSC-CMs
may be used for follow-up studies, including instances where
there is discordance between ion channel/in silico and clinical
ECG findings, or when high clinical exposures are not possible
in human ECG studies.
Prior studies13-15 were reviewed and results from the CiPA
Myocyte Validation Study8 were presented. The study design issummarized as follows:








10 sites;
5 electrophysiological devices: 4 microelectrode array(MEA) devices and 1 voltage-sensitive dye (VSD)
device;2 hiPSC-CM lines: iCell2 (10 data sets), and Cor.4U(5 data sets);
28 blinded drugs, 4 concentrations, 5-6 replicate wells at
each concentration, acute effects (30-minute drug exposure); andstandard proprietary media throughout: serum containing (MEA sites), serum-free (VSD site).

Seven hiPSC-CM predictors were evaluated in Torsade risk
categorization models. The 3 that proved to be most useful for
drug categorization were the following:

the presence of drug-induced cellular arrhythmias (repolarization abnormalities) at any concentration;
maximum drug-induced repolarization change (ms)observed at any concentration; anddrug-induced repolarization change (ms) at Cmax.

Regarding the first of these useful predictors, most lowrisk drugs did not induce any cellular arrhythmias. A few
sites showed arrhythmias for ranolazine, metoprolol, andmexiletine. Few arrhythmias were reported for intermediate-5
risk drugs risperidone, terfenadine, clozapine, and chlorpromazine, but most of the data sets showed arrhythmias induced bydomperidone, ondansetron, droperidol, pimozide, clarithromycin, astemizole and cisapride. Most high-risk drugs induced
arrhythmia-like events, with bepridil being an exception.
Regarding the second predictor, all high-risk drugs induced
repolarization prolongation. Drug-induced change in repolarization for intermediate-risk drugs was less than for high-riskdrugs. Five of 9 low-risk drugs induced repolarization duration
shortening, and while the other 4 prolonged repolarization, theaverage across all 9 drugs was a shortening. Regarding the third
predictor, estimated drug-induced repolarization prolongation
at Cmax was highest for ibutilide, dofetilide, quinidine, andvandetanib in the high-risk category; the category averagereflected substantial prolongation. Intermediate-risk drugs
induced moderate repolarization prolongation at Cmax. Fiveof 9 drugs in the low-risk category shortened repolarization
duration at Cmax, while drug-induced prolongation wasobserved for the other 4. The average change in repolarization
duration in this category reflected a small shortening.
Two models of Torsade risk prediction were employed.
Model 1 compared the probability of predicting high- plus
intermediate-risk drugs versus low-risk drugs. The associated
ROC-AUC value for all 28 drugs was 0.87 (Good rating).
Model 2 compared high- versus low-risk drugs, as well asintermediate- versus low-risk drugs. The associated ROCAUC for high- versus low-risk (0.92) fell in the Excellent
rating range, and the ROC-AUC for intermediate- versus
low-risk (0.81) fell in the Good rating range. Variability bysite was lower than that introduced by cell type, and both werelower than the contribution provided by drug concentration(all assessed as fixed effects). Site-induced variability was
lower than all other sources of random variability, demonstrating the ability of hiPSC-CMs to reproducibly detect
concentration-dependent effects of single and multiple ionchannel–blocking activity.

Phase 1 ECG Biomarker(s) Under CiPAThe role of phase 1 ECG data under CiPA is to determine if
there are unexpected human in vivo ion channel effects compared with nonclinical ion channel data. Identification and validation of ECG signatures that facilitate such determination are
therefore important considerations. As noted previously,
J-Tpeakc represents early repolarization and is of particularinterest in the present context.
Three previous investigations set the scene for the work
reported in this domain. In an initial analysis of 34 drug safety
clinical studies,16 J-Tpeakc was identified as an ECG biomarker that could differentiate drugs with predominant hERG
block from those associated with hERG block combined withcalcium and/or late sodium block (ie, balanced ion channel
blockers). Predominant hERG blockers prolonged J-Tpeakc,
while balanced ion channel blockers had no effect or a shortening effect on J-Tpeakc. A prospective clinical trial involving

6
dofetilide, quinidine, ranolazine, and verapamil confirmed thisfinding. Dofetilide and quinidine, which are predominantly
hERG channel blockers and high Torsade risk drugs, prolonged
J-Tpeakc; in contrast, ranolazine and verapamil, which arebalanced ion channel blockers with hERG þ late sodium blockand hERG þ calcium block, respectively, did not prolong JTpeakc.17
A second prospective clinical study addressed whether it
was possible to re-create the ECG signature of ranolazine bycombining a predominant hERG blocker, dofetilide, with alate sodium blocker, mexiletine or lidocaine.18 The combination of a hERG blocker with a late sodium blocker was able tore-create the ECG signature of ranolazine, a low-risk drug thatblocks both of these ion channels at clinically relevant exposures. In a separate arm of this study, diltiazem (calcium
block) did not shorten QTc prolongation by moxifloxacin(predominant hERG block), a result which may have been
confounded by study design.
Results of a new prospective clinical trial were presented at
the Think Tank. The primary objective of Part 1 was to confirm
that concentration-response analysis of the electrocardiographic QTc and J-Tpeakc intervals in phase 1 clinical pharmacology studies can be used to confirm that balanced ionchannel–blocking drugs (ranolazine, verapamil, and lopinavir/ritonavir) do not cause J-Tpeakc prolongation, while a predominantly hERG-blocking drug (chloroquine) does cause
J-Tpeakc prolongation.8 As expected, the balanced ion channel
blockers (ranolazine, verapamil, and lopinavir/ritonavir) prolonged QTc and had flat or negative J-Tpeakc slopes. For verapamil and lopinavir/ritonavir, the upper bounds of therespective 90% confidence intervals for J-Tpeakc fell below
the prespecified threshold of 10 ms, but ranolazine’s upperbound was 2 ms above this threshold. The predominant hERG
blocker (chloroquine) had an upper bound above 10 ms for bothQTc and J-Tpeakc as expected, although J-Tpeakc prolongation with chloroquine was not concentration-dependent.
Part 2 of the study investigated whether diltiazem (calcium
block) reduces the QTc prolongation by dofetilide (predominantly hERG block) by shortening J-Tpeakc. In combination
with dofetilide, diltiazem did not shorten QTc, but shortened
J-Tpeakc. While this ECG signature of diltiazem þ dofetilide
differed from what was observed in the prior study for latesodium current block (mexiletine or lidocaine) when combined with hERG potassium channel block (dofetilide), verapamil and lopinavir/ritonavir (which have hERG andcalcium block) prolonged QTc without prolonging J-Tpeakc
in Part 1 of the study.

Summary
CiPA has multiple goals and potential areas to impact drug
discovery and development. These include (1) aiding candidate
selection early in drug discovery; (2) enabling mechanistic
studies so promising new drugs that have a false positive signalof potential risk (eg, hERG block or modest QTc prolongation)

Therapeutic Innovation & Regulatory Science XX(X)
are not prematurely discontinued; (3) enabling a pathway forde-risking of drugs with demonstrated hERG block and/or QTc
prolongation (in nonclinical or clinical studies) and, whenappropriate, not requiring intensive ECG monitoring in phase
3 trials; and (4) providing more informative drug labels for new
drugs and currently marketed drugs. Points 3 and 4 were also
discussed at an FDA Advisory Committee in 2017,5 where thecommittee voted 11 to 2 in favor of CiPA being fit-for-purpose
for determining whether ECGs need to be collected in phase 3
trials and informing proarrhythmic risk language in drug labeling, assuming the validation studies were positive, as they are.
The Think Tank had multiple presentations of study results,
and also intensive discussion around these topics, with different
viewpoints expressed during main sessions on day 1 (May 21)
and subsequent break-out sessions, each focusing on one of the
CiPA components, on day 2 (May 22). The purpose of thispaper is not to capture the discussions, but rather to focus onsummarizing the core CiPA studies that were presented at themeeting. Additional details will need to be resolved, such as
how to handle potential interactions among multiple QTcprolonging drugs, including in labeling.
Additional presentations commented on how CiPA isalready being implemented in a regulatory setting to permit
an alternative risk assessment for drugs with significant heart
rate increases or other factors that confound QTc assessment;
FDA has already recommended this approach to multiple sponsors with drugs in development. In addition, standardized nonclinical assays may be able to reduce the exposure margins inphase 1 QTc assessments and permit an earlier assessment ofproarrhythmic risk for phase 1 oncology drugs, for which current studies are only able to rule out a 20-ms QTc effect.
As a final summary note, results from validation endeavors
for each of the CiPA components were presented, thus setting
the stage for further discussion with the ICH S7B/E14 Discussion Group.
Author Note
This article reflects the views of the authors and should not be construed to represent FDA’s views or policies.

Acknowledgments
The authors thank all presenters from the Think-Tank Meeting. Presentations are available at: http://www.cardiac-safety.org/2018/05/
new-advances-in-the-assessment-of-drug-induced-arrhythmias-andthe-comprehensive-in-vitro-proarrhythmia-assay-cipa/

Declaration of Conflicting Interests
Dr Strauss reports no relevant disclosures. Dr Gintant is an employeeof AbbVie, a pharmaceutical company involved in new drug development. Dr Li reports no relevant disclosures. Dr Wu reports norelevant disclosures. Dr Blinova reports no relevant disclosures. Dr
Vicente reports no relevant disclosures. Dr Turner is an employee of
DRT Strategies, Inc. Dr Sager has disclosed consulting relationships
with ERT, BioTelemetry Research, ICardiac, Charles River, andAnaBios.

Strauss et alFunding
No financial support of the research, authorship, and/or publication ofthis article was declared.

ORCID iD
David G. Strauss, MD, PhD http://orcid.org/0000-0003-3023-3297
Jose Vicente, PhD
http://orcid.org/0000-0001-9963-1205

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