phuse · 2016. 3. 24. · project: analysis and display white papers project team title: analyses...

Project: Analysis and Display White Papers Project Team Title: Analyses and Displays Associated with TQT Studies

Working Group: Standard Analyses and Scripts Working Group

PhUSE

PhUSE Computational Science Development of Standard

Scripts for Analysis and Programming Working Group

Analysis and Display White Papers Project Team

Analyses and Displays Associated with Thorough QT/QTc

Studies

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Table of Contents 1. DISCLAIMER ................................................................................................................................................... 4

2. NOTICE OF CURRENT EDITION ........................................................................................................................ 4

3. ADDITIONS AND/OR REVISIONS ..................................................................................................................... 4

4. OVERVIEW: PURPOSE AND SCOPE ................................................................................................................. 4

5. PROJECT BACKGROUND ................................................................................................................................. 6

6. ECG BACKGROUND ......................................................................................................................................... 7

7. PRE-ANALYTICAL ISSUES................................................................................................................................. 9

7.1 CORRECTION OF THE QT INTERVAL FOR HEART RATE ............................................................................................ 9 7.1.1 Historical Population-Based Formula from a Historical Population .................................................... 9 7.1.2 Study Population-Based Formula from the Population under Study ................................................. 11 7.1.3 Individual-Based Formula (QTcI)..................................................................................................... 11 7.1.4 Choosing the Right Correction Method ........................................................................................... 12

7.2 THOROUGH QT (TQT) STUDY DESIGN ............................................................................................................ 12 7.2.1 Brief Background ........................................................................................................................... 12

7.2.1.1 Historical Reason for the TQT Study ...........................................................................................................12 7.2.1.2 Study Design Background Considerations ...................................................................................................13 7.2.1.3 Days of ECG Collection and Time Points of ECG Collection on the Days of Collection ....................................14

7.2.1.3.1 Collection of ECGs for Baseline..............................................................................................................14 7.2.1.3.2 Collection of On-Treatment ECGs ..........................................................................................................15 7.2.1.3.3 ECGs and Their Data on Days of Collection (Baseline and On-Treatment) ...............................................16

7.2.2 Specific Designs ............................................................................................................................. 17 7.2.2.1 Parallel Studies ..........................................................................................................................................17 7.2.2.2 Crossover Studies ......................................................................................................................................18 7.2.2.3 Non-standard Designs ................................................................................................................................19

7.3 BASELINE AND TREATMENT DIFFERENCE (DRUG EFFECT) ..................................................................................... 19 7.3.1 Time-Matched Lead-in Day Baseline; Double-Delta Treatment Difference ....................................... 20 7.3.2 Time-Averaged Lead-in Day Baseline; Double-Delta Treatment Difference ...................................... 20 7.3.3 Predose Averaged Baseline; Double-Delta Treatment Difference .................................................... 21

8. ANALYSIS ..................................................................................................................................................... 22

8.1 PRIMARY ANALYSIS .................................................................................................................................... 22 8.1.1 Testing of QT Prolongation ............................................................................................................ 22

8.1.1.1 Multiplicity Issues ......................................................................................................................................22 8.1.2 Assay Sensitivity ............................................................................................................................ 23

8.1.2.1 Multiplicity Issues ......................................................................................................................................24 8.1.3 Categorical Analyses ...................................................................................................................... 25 8.1.4 Morphological (Qualitative) Analyses ............................................................................................. 25 8.1.5 Exploratory Analysis of Other Continuous ECG Parameters ............................................................. 26

8.2 CONCENTRATION-RESPONSE RELATIONSHIP (CRR) ............................................................................................ 26 8.2.1 Rationale for Performing a Concentration-Response Analysis within a TQT Study ........................... 26

8.2.1.1 When Results of the TQT Study Are Negative (Non-inferiority Is Supported by the Results) .........................26 8.2.1.2 When Results of the TQT Study Are Positive (Cannot Reject Inferiority based on Study Results) ...................27 8.2.1.3 When Assay Sensitivity Is Not Demonstrated ..............................................................................................27

8.2.2 Methodology ................................................................................................................................. 27 8.3 P-VALUES AND CONFIDENCE INTERVALS .......................................................................................................... 28

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9. LIST OF OUTPUTS ......................................................................................................................................... 30

10. OUTPUTS SHELLS ...................................................................................................................................... 31

11. ACKNOWLEDGEMENTS ............................................................................................................................. 50

12. PROJECT LEADER CONTACT INFORMATION .............................................................................................. 50

13. REFERENCES ............................................................................................................................................. 51

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1. Disclaimer The opinions expressed in this document are those of the authors and do not necessarily represent the opinions of Pharmaceutical Users Software Exchange (PhUSE), the members’ respective companies or organizations, or regulatory authorities. The content in this document should not be interpreted as a data standard and/or information required by regulatory authorities.

2. Notice of Current Edition This edition of the “Analyses and Displays Associated with Thorough QT/QTc Studies” is the first edition.

3. Additions and/or Revisions Date Author Version Changes

2016-March-11 See Section 12 v1.0 First Edition

4. Overview: Purpose and Scope Under the Clinical Data Interchange Standards Consortium (CDISC), standards have been defined for data collection (Clinical Data Acquisition Standards Harmonization - CDASH), tabulation (Study Data Tabulation Model - SDTM), and analysis (Analysis Data Model - ADaM) datasets. The next step is to develop standard tables, figures, and listings (TFLs). The Development of Standard Scripts for Analysis and Programming Working Group is leading an effort to create several white papers providing recommended analyses and displays for common measurements and has developed a script repository as a place to store shared code.

The purpose of this white paper is to provide advice on displaying, summarizing, and analyzing data from a Thorough QT/corrected QT (QTc) Study (also referred to as a TQT study). The intent is to begin the process of developing industry standards with respect to analyses and reporting for these trials. In particular, this white paper provides recommended processes for:

• Pre-analytical issues: Study design, QT interval corrections, and Baseline adjustments • Analytical issues: Testing for QT prolongation, Assay sensitivity, Outlier analysis / Categorical analysis,

Morphological (Qualitative) abnormalities, and pharmacokinetic (PK)/pharmacodynamics (PD) analysis

This paper attempts to give recommendations for difficult decisions related to the analysis of difficult topics such as QT interval correction, baseline, and PK/PD analysis. Because there are on-going discussions regarding these topics, the recommendations made here are mainly based on the authors experience with these trials and submissions to regulatory bodies (and International Conference on Harmonisation [ICH]-E14 guidelines and Q&A at the time this white paper was written).

The content of this document can be used when developing the analysis plan for TQT studies.

Development of standard TFLs and associated analyses will lead to improved standardization from collection through data storage, as it is necessary to determine how the results should be reported and analyzed before finalizing how to collect and store the data. The development of standard TFLs will also lead to improved product lifecycle management, by ensuring reviewers receive the desired analyses for consistent and efficient evaluation of patient safety. Although having standard TFLs is an ultimate goal, this white paper reflects recommendations only, and should not be interpreted as “required” by any regulatory agency.

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Detailed specifications for TFL or dataset development are considered out of scope for this white paper. However, the hope is that specifications and code (utilizing SDTM and ADaM structures) will be developed consistent with the concepts outlined here, and placed in the publicly available scripts repository.

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5. Project Background The beginning of the effort leading to this white paper came from the initiation of the FDA/PhUSE Computational Science Collaboration, a yearly conference and ongoing working groups to support addressing computational needs of the industry. The FDA identified key priorities and teamed up with PhUSE to tackle various challenges using collaboration, crowd sourcing, and innovation (Rosario LA 2012). The FDA and PhUSE created several computational science (CS) working groups to address several of these challenges. The working group, titled “Development of Standard Scripts for Analysis and Programming,” has led the development of this white paper, along with the development of a platform for storing shared code. As noted in Section 1, the content in this document should not be interpreted as a data standard and/or information required by regulatory authorities.

Members of the Analysis and Display White Papers Project Team reviewed regulatory guidance and shared ideas and lessons learned from their experience. Draft white papers were developed and posted in the PhUSE wiki environment for public comment.

Most contributors and reviewers of this white paper are industry statisticians, with input from non-industry statisticians (e.g., FDA and academia) and industry and non-industry clinicians. Additional input (e.g., from other regulatory agencies) for future versions of this white paper would be beneficial.

Several existing documents contain suggested TFLs for common measurements. Some of the documents are now relatively outdated, and generally lack sufficient detail to be used as support for the entire standardization effort. Nevertheless, these documents were used as a starting point in the development of this white paper. The documents include:

• ICH E3: Structure and Content of Clinical Study Reports • Guideline for Industry: Structure and Content of Clinical Study Reports • Guidance for Industry: Premarketing Risk Assessment • Reviewer Guidance. Conducting a Clinical Safety Review of a New Product Application and Preparing a

report on the Review. • ICH M4E: Common Technical Document for the Registration of Pharmaceuticals for Human Use –

Efficacy • ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential For Non-

Antiarrhythmic Drugs • ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-

antiarrhythmic drugs Questions and Answers R1. • ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-

antiarrhythmic drugs Questions and Answers R3. • FDA Guidance for Industry: ICH E14 Clinical Evaluation of QT/QTc. Interval Prolongation and

Proarrhythmic Potential for Non-Antiarrhythmic Drugs. • QT Studies Therapeutic Area Data Standards User Guide (TAUG) V1. CDISC.

The ICH E14 guidelines, FDA Guidance for Industry and TAUG are considered key documents. However, they do not provide detailed information that would enable standardization of all analysis and presentation of TQT studies.

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http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E3/E3_Guideline.pdf

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm073113.pdf

http://www.fda.gov/ohrms/dockets/ac/04/briefing/2004-4068B1_10_GFI-Premarketing-Risk-Assessment.pdf



http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/CTD/M4__R1__Efficacy/M4E__R1_.pdf

http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/CTD/M4__R1__Efficacy/M4E__R1_.pdf





http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf

http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM073161.pdf

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM073161.pdf

http://www.cdisc.org/system/files/members/standard/TAUG-QT_v1_0.pdf



6. ECG Background Some basic understanding of electrocardiograms (ECGs) can be helpful in planning and completing analyses for TQT studies. The ECG is a graphical representation of the electrical depolarization and repolarization of the heart’s cells that initiates and spreads through the heart in an organized manner and causes contraction of the heart muscle that results in the pumping of blood. In 1895, Einthoven established the five primary topographic features of the ECG tracing (P, Q, R, S, and T waves; discussed in more detail below), and in 1912 he defined the now standard ECG leads (the waveform of potential difference over time between two sets of one or more electrodes attached to the body) I, II, and III. Additional standard leads were established in 1938 (V1 – V6) and in 1942 (aVR, aVL, and aVF). Therefore, the standard ECG records this activity at the body surface for 12 leads (I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6). A continuous waveform (positive and negative changes over time) of electrical activity is recorded for each lead. A standard ECG is a 10-second recording, but ECG data can be recorded and stored digitally for any amount of time (limited only by storage media capacity). A standard paper ECG displays 2.5 seconds of each lead (4 sets of 3 leads) and all 10 seconds of one lead as illustrated in Figure 6-1. In Figure 6-1, an ECG recoding of 10 seconds is displayed. The fourth (bottom) line tracing is the entire 10 seconds of the ECG data recorded from Lead II (referred to as the “Rhythm Strip”; Lead II is the customary “Rhythm Strip” Lead, but other leads might be selected as the “Rhythm Strip” Lead). The 3 line tracings above the “Rhythm Strip” display shorter time segments of all 12 leads – Leads I-III simultaneously moving down from the first to third line tracing; then leads aVR and aVF; then Leads V1-V3; and finally V4-V6.

Figure 6-1: Standard 10-sec ECG The waveforms are a series of complexes (a complex and its component parts are shown in Figure 6-2) that represent the sequential depolarization and repolarization electrical activity that spreads through the heart. These complexes have parts, briefly noted above, that are named as shown in Figure 6-2. Note that a single complex contains a P wave, a QRS complex (that consists of a Q wave [sometimes absent], an R wave, and an S wave; the R and S waves can have opposite polarities across leads), a T wave, and sometimes a U wave. Each of these

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complexes represents a complete depolarization and repolarization of the heart. There is an isoelectric gap (no electrical activity) between complexes. The RR interval, not shown in Figure 6-2, is the time between successive R waves (and, therefore, the time between complexes). Analyses in TQT studies will focus on the QT interval and the RR interval or heart rate (HR), respectively (see below), but secondary analyses will also be conducted on the PR interval and the QRS complex. The width of the waves, and intervals, including the RR interval, represent time and are most commonly expressed in millisecond (msec) units. Heart rate, which is the number of complexes per minute, is usually expressed as beats per minute (bpm). Therefore, the RR interval measurement, in msec, and HR, in beats/min, have the following relationship:

• RR = (1/(HR/60))*1000 • HR = 60,000/RR

Figure 6-2: A single ECG waveform complex and its parts

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7. Pre-analytical Issues 7.1 Correction of the QT Interval for Heart Rate The QT interval is a measure or biomarker for the time of ventricular depolarization and repolarization to occur, but in practice, it is used as a biomarker for the time of ventricular repolarization. The QT interval changes in inverse relationship to HR for appropriate physiological coordination of the pumping of blood by the heart. Therefore, because subjects’ heart rates are not constant throughout participation in a TQT study (or when evaluated clinically), it is necessary to correct the QT interval for HR in order to make comparisons of the QT interval recorded at different HRs at different times. Complicating the situation a bit more is the fact that the QT interval does not change instantaneously with a change in HR. The change in QT interval is delayed; its change is subject to hysteresis. Hysteresis is generally ignored in the analysis of TQT studies, but researchers like Malik et al (2008) have developed methods for evaluating hysteresis patterns of the QT interval in response to HR changes (in contrast to the hysteresis of QT change with a drug that does change QT) on an individual basis and incorporating them into QT correction. Discussion of this topic is beyond the scope of this white paper. The ideal QTc interval would be uncorrelated with HR or the RR interval. In other words if QTc were plotted against either RR interval or HR, and the data were fit to a linear model, the slope of the regression line would be “0”. Essentially, QT correction for HR attempts to adjust the individual subject’s QT interval, at any HR, to a value that would be expected if the subject’s HR were constant. In the majority of QT correction formulas, RR interval is used rather than HR because RR interval is measured and expressed in the same units as the QT interval, msec, whereas HR is measured and expressed in beats/min as illustrated above. In general, there are three basic methods to adjust or correct the QT interval for HR (RR-interval). The methods are:

1. Historical population-based formulas derived from historical populations 2. Study population-based formulas derived from the population under study 3. Individual-based formulas derived for each individual in the population under study

All three methods are based on exploring the mathematical relationship between the QT interval and the RR-interval, but they use different populations for finding this relationship. The exploration of this mathematical relationship amounts to finding a function and its numerical coefficients, or finding the specific numerical coefficient(s) for either a prespecified function or best fitting mathematical function (linear or nonlinear) from among a number of functions that model the relationship between the QT interval and the RR-interval for a set of ECGs from a population of multiple individuals (or from one individual in the case of individual-based formulas). The mathematical function is then translated into a correction formula, using the numerical coefficients that were found in the data fitting process. The same formula is subsequently applied to all ECGs for which a QTc is being computed. Therefore, for example, a set of QT interval measurements and associated RR-interval measurements could be fitted to the data using the mathematical function:

QT = β * RRα

The value of the coefficient α that is found to give the best fit for the data might be 0.25. Then the correction formula for QTc (in seconds) would be: QTc = QT / RR0.25, where QT and RR are measured in seconds.

7.1.1 Historical Population-Based Formula from a Historical Population In a historical population, this would be a group of normal, healthy persons, generally with one ECG from each person. Due to the normal variance between different populations, multiple researchers using different groups of subjects have derived different formulas, even when fitting their respective data to the same mathematical function.

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The most commonly used historical population-based correction formulae were proposed in 1920 by Bazett (QTcB) and separately by Fridericia (QTcF). Unfortunately, each formula can lead to bias for some clinically relevant values of HR, as illustrated below. For an extensive list of 31 such historical correction formulas, including those listed below, based on multiple mathematical functions, see a manuscript by Malik (2002a). As indicated above, each of these formulas could be expressed using HR, where RRmiliseconds = ((1/(HRbeats-per-minute/60))*1000)

(i) Bazett (obtained in seconds, where QT and RR are measured in seconds): QTc = QT/ RR1/2

(ii) Fridericia (obtained in seconds, where QT and RR are measured in seconds): QTc=QT/RR1/3 (iii) Framingham: QTc=QT+(0.154*(1-RR))

(iv) Van de Water: QTc=QT–((0.087*(1-RR))

It is reasonably well known that the Bazett formula over-corrects at faster HRs (over 60 beats/min) and conversely under-corrects at slower HRs. That is, at faster HRs (smaller RR- intervals), the computed QTc is ”larger than it should be”, and at slower HRs (larger RR-intervals), the computed QTc is ”smaller than it should be”. When Bazett’s QTc is plotted against RR interval and a regression line is plotted, the slope is negative (Figure 7-1; with a perfect correction, the slope of the regression line would be “0” as described above). In spite of this, Bazett’s formula is still the most widely used for clinical correction of QT intervals. However, it is becoming more acceptable in regulatory documents to use the Fridericia formula correction, without use of the Bazett formula (ICH E14, 2012; Question 11), along with additional correction results as described below.

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Figure 7-1: Relationship between the Bazett- and Fridericia-Corrected QT Interval and RR Interval (Note that the solid line is not the linear regression line but the mean of QTc values at each RR value)

7.1.2 Study Population-Based Formula from the Population under Study A study population formula derived from the population under study uses off-treatment, baseline ECGs, and sometimes ECGs collected during placebo treatment to construct a population correction formula as described above. The method is based on finding the specific numerical coefficient(s) for either a prespecified or best fitting mathematical function (linear or nonlinear) that models the relationship between the QT interval and the RR interval for a set of ECGs from a population of multiple individuals. The mathematical function is then translated into a correction formula using the numerical coefficients that were found in the data fitting process. The same formula is then applied to all ECGs for which a QTc is being computed. Because the formula is based on the behavior of the individuals actually under study, such a study population-derived formula presumably accounts for variables (e.g., disease factors, age, and gender distribution) which might influence the QT-RR relationship. Therefore, such a formula should be more accurate for the individuals under study than one based on a historical population.

7.1.3 Individual-Based Formula (QTcI) It has been well established that the mathematical function that best describes the QT-RR relationship may differ from individual to individual (Malik et al., 2002b) but is stable within individuals, and, therefore, any group-based (study-wide) correction will be somewhat imprecise when applied to individuals. Though the magnitude of imprecision is generally not of sufficient magnitude to affect negatively the TQT study substantially, it is possible to derive and use individual-based correction formulas. An individual-based QTc (QTcI) requires that a number of ECGs be obtained across a sufficient range of HRs. The number of ECGs required for individual correction is an important matter. Morganroth (2005) has suggested that 35 to 50 ECGs covering a range of HRs of 50 to 80 beats per minute for each individual under baseline (non-treatment) conditions are sufficient. The HR range for ECGs collected for use in computing individual corrections should more appropriately include rates that will be observed during treatment with the experimental drug to be tested in the TQT study. For such drugs that substantially alter heart rate, that might be impossible under baseline conditions (see last paragraph of this section below and Garnett et al. [2012]). Couderc (2005) has published data to support the position that at least 400 ECGs (QT-RR pairs for each individual subject) are needed to compute an adequate individual correction, and that there must definitely be a

Bazett correctionCo

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T in

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RR interval (sec)0.6 0.8 1.0 1.2

Fridericia correction

Corre

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RR interval (sec)0.6 0.8 1.0 1.2

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range of heart rates corresponding to the heart rates that will be observed with the experimental drug. Using continuous ECG recording, obtaining these large numbers of baseline ECGs can be easily achieved. The ECGs collected during placebo-treatment can be considered for use in computing individual corrections but this might be considered controversial by some regulatory agencies. Though these ECGs are not influenced by drug, they are potentially influenced by a distinct set of circumstances relative to those collected at baseline. They are collected with the subject knowing that they are under some treatment, rather than under no treatment and these psychological differences might, hypothetically, result in subtle difference in autonomic tone that would influence the QT-RR relationship. These QT-RR data are then used to compute a specific correction formula for each individual subject, in a manner similar to that used to compute a population correction. In computing QTcI, one sub-approach is to use a single, predetermined mathematical model for all subjects and we can refer to this approach as individualized correction (optimizing the coefficient[s] on an individual basis for a single correction formula). An alternative sub-approach is to fit the individual subject’s data to several preselected mathematical models, and use the best mathematical model for each individual subject (model with the best fit to the data and that results in flattest regression line after correction (QTcI verus RR)) and we can refer to this approach as individualized individual correction (optimizing the actual correction formula and its coefficient[s] on an individual basis). Malik et al. (2004) have described 12 mathematical models that could be considered when finding an individual best-fit model for a given subject. As such, this latter method for computing QTcI, individualized individual correction, is probably the best using sufficient baseline data (400 ECGs), with an appropriate range of heart rates as will be observed with test drug. However, either type of individual correction formula computation is also very labor intensive and costly (ECG collection and computation) to use. Some researchers have developed methods of assessing changes in ventricular repolarization based on the QT interval, which do not rely on an explicit correction of the QT- interval for HR (the RR-interval). These methods are particularly important when the experimental drug results in marked changes in autonomic nervous system tone and HR. These changes can be so large, that it will be difficult to obtain off-treatment ECG data at heart rates that will be observed during treatment with the experimental drug, which would raise concerns about the validity of any correction factor. Discussion of these alternatives beyond the introduction of the concept is outside the scope of this document, but can be reviewed in the manuscript by Garnett et al. (2012). These methods would generally rely on continuous recording data.

7.1.4 Choosing the Right Correction Method As discussed above a number of correction methods can be used for the QT interval. In case of multiple correction methods being available for a study, usually one is pre-selected and considered as primary in the statistical analysis plan; in which case the benefits/problems of the methods as outlined above can be considered to make a choice. Regardless of the pre-selection, it is not unreasonable to investigate after the data are received which correction method is the best fit to the data (especially in cases where different correction methods provide different results). This discussion is considered out of scope for this white paper but the authors consider the following paper, Darpö et al. (2012), as a good place to start.

7.2 Thorough QT (TQT) Study Design

7.2.1 Brief Background 7.2.1.1 Historical Reason for the TQT Study

Jervell and Lange-Nielsen (1957) described correlations between hereditary long QT intervals and sudden death. Smirk and Palmer (1960) noted that initiation of ventricular depolarization (R waves) prematurely occurring before the complete repolarization of the ventricle following the preceding depolarization (during the T waves – referred to

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as “R-on-T Pattern”) increases the risk of ventricular arrhythmia. Torsade de pointes (TdP), a specific type of ventricular tachyarrhythmia (fast arrhythmia), was first described in a publication by Dessertenne (1966). Although some drugs that had been developed as anti-arrhythmic agents also altered ventricular repolarization as evidenced by prolonged QTc, it was not widely appreciated that non-cardiac drugs could also have this property. The use of non-sedating antihistamines, e.g., terfenadine and astemizole, from 1985 to 1999 provided an important case study of the public health issues with the widespread use of non-cardiac drugs with such cardiac effects. Initial reports of cardiac arrhythmias, including TdP, were predominately associated with high blood concentrations of these antihistamines subsequent to overdose. Given the metabolic pathway of these drugs, arrhythmias were eventually reported subsequent to coadministration with drugs and substances that slowed the metabolism of terfenadine and astemizole, including grapefruit juice (also resulting in high blood concentrations). Despite warning letters to physicians and restricted product labeling in 1992, inappropriate medications continued to be coadministered with these drugs. Both drugs were withdrawn in 1999 from use in the United States (US) after safer alternatives were developed. The high visibility of the association between non-sedating antihistamines and fatal ventricular arrhythmias prompted extensive research into the mechanisms by which drugs cause these cardiac arrhythmias. Although many details remain unknown, current research suggests that most drugs with strong arrhythmic potential interfere with a specific potassium channel in cardiac muscle fiber that functions to repolarize the muscle fiber cells. Partially or completely blocking the potassium channel results in delayed repolarization of the muscle fiber cells. Delayed repolarization increases the time required to restore the normal resting potential prior to the next depolarization for the next muscle contraction. Arrhythmias such as TdP are possibly triggered by the initiation of the R waves (beginning the depolarization of the ventricles) during the period of delayed repolarization, while the ventricles are still partially depolarized. In summary, drugs that delay ventricular repolarization might place a person at increased risk of a fatal ventricular arrhythmia. Again, delayed ventricular repolarization is manifested on the ECG tracing as a prolonged QTc. The QTc is clearly recognized as an imperfect biomarker for increased risk of fatal arrhythmia because it can be increased by a number of drugs that are not associated with a significant incidence of such arrhythmias. None the less, an increase in QTc is considered an important risk factor and any drug-induced increase is considered important to assess and quantify. On an individual basis, the increase in QTc generally needs to be substantial to place the patient at risk, but for a potential new drug, even a slight mean increase1 in QTc can be clinically meaningful, in that some degree of risk cannot be excluded in a small number of individuals in a large population that will receive the drug during its use in clinical medicine. The TQT study is considered the most precise way of studying the potential drug effect on QTc in human subjects.

7.2.1.2 Study Design Background Considerations

Clinical studies to detect QTc mean increases as small as 5 msec face significant challenges because of the substantial variability in QT intervals. The first source of variability is the process of acquiring and measuring the QT interval. Placement of ECG electrodes, choice of lead(s) to be measured, standardization of ECG machines, choice of media (paper versus digital), and variability in expert measurement of the QT interval comprise critical components of the process. QT intervals are characterized by substantial inter- and intra-subject variability apart from that engendered by acquisition and measurement. Sources of inter-subject variability can include a genetic predisposition to long QT intervals, electrolyte concentrations, autonomic activity, age, and sex. Intra-subject variability is strongly influenced by diurnal rhythms (transitioning to sleep from wakefulness and vice versa) that influence autonomic tone and heart rate.

1 >5 milliseconds (msec) would be considered to exceed random variability (Malik, 2001) and a mean increase of ≥10 msec could be of regulatory interest (ICH, 2005).

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Dose selection, duration of dosing, timing of ECG measurements, patient population, and control of factors influencing variability will need to be addressed in any study designed to evaluate QT interval. Though the TQT study is considered the most definitive study of the potential influence of a drug on QT interval, it might suffer from limitations due to sample size, the health of the subject population, and many other factors that cause the drug administration in the study to be different from how the drug will be used in broad clinical practice. This brief background provided below is informed primarily by the May 2005 ICH-E14 document [ICH, 2005], that describes the basic conduct, purpose, and expected analyses of the TQT study, as well as by its update in a subsequent Q&A document (ICH, 2012) The purpose of a TQT study is to evaluate the potential for an experimental drug to delay cardiac ventricular repolarization, which the study does through evaluation of changes in QTc during drug treatment; and also to demonstrate that the study is capable of detecting differences in the variability that can be observed during placebo treatment (random variability; approximately 5 msec), so as to confirm that any lack of detected change is due to actual lack of change rather than lack of assay sensitivity as assessed by a positive control that causes a slight increase in QTc (ideally in the 5- to 10-msec range). These TQT studies are generally conducted in healthy volunteers, which are highly screened for normal cardiac electrical activity for ease of precise measurement of the QT- interval and to avoid additional confounding factors. The TQT study designs can be a crossover or a parallel design discussed in more detail below. In general, the treatments are:

1. A dose of the experimental drug that is several times higher, if possible, than the intended maximum therapeutic dose, in order to account for drug-drug interactions and/or genetic metabolic enzyme deficiencies that might lead to greater exposure to the experimental drug than otherwise intended with a given dose during routine clinical use

2. Placebo 3. A positive control for purpose of demonstration of assay sensitivity (most often moxifloxacin, usually oral

but sometimes intravenous) 4. Optionally, a dose of the experimental drug that is within the intended therapeutic range (generally the

maximum intended therapeutic dose) The administration of the active control has been allowed by regulators to be open-label, but the administrations of the experimental drug dose(s) and placebo are double-blind, and ECG measurements and readings are performed by persons completely blinded to associated treatments, subject details, and date/time of the ECG.

7.2.1.3 Days of ECG Collection and Time Points of ECG Collection on the Days of Collection

The ECGs are collected as a set of replicates (in close temporal proximity, e.g., 3 ECGs collected at 1-minute intervals) of 10 seconds in duration and utilizing all 12 leads. In analyses, the QTc values of the replicates will be averaged before analysis of differences in changes in QTc to reduce the signal-to-noise ratio and improve the accuracy of the measurement. When discussing the collection of ECGs below, “ECG” will refer to the set of replicate ECGs. The ECGs can be collected as conventional ECGs, or they can be extracted from a continuous high fidelity ECG recording. Experimental drug and metabolite concentrations are often collected for assay immediately after the time of ECG collection for PK/PD analysis, which can be a useful secondary analysis pertinent to the potential influence of the experimental drug on ventricular repolarization. The timing and collection of replicate ECGs are guided by the known properties (e.g., PK) of the drug and its metabolites.

7.2.1.3.1 Collection of ECGs for Baseline

In general, in the analyses of QTc, baseline QTc values are subtracted from on-treatment QTc values to create a “single ∆” value that is “change in QTc” and this “change in QTc” is compared between active treatments and

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placebo. Because several alternative mathematical/statistical definitions of baseline exist, Section 7.3 describes alternative analyses that are in large part influenced by the definition of baseline. Baseline ECGs may be collected:

• On the day (or for multiple days) preceding the day of first dose administration of each treatment; If this type of baseline is used, ECGs are collected at multiple time points that match the

time points at which ECGs are collected on-treatment. If ECGs are collected on multiple days, then QTc values from those days can be averaged

for the baseline value used in analyses; this multiple baseline day collection is rarely done.

• The averaging can be for each time point when a time-matched baseline is being used (time-matched) or across all time points (time averaged), if a time averaged baseline is being used (see Section 7.3 for a more detailed description of baseline alternatives).

Baseline day(s) and time points are the same for each treatment to maintain the blind. Although consideration might be given to using a single, common baseline for each

treatment in a crossover study, either before the first treatment period for all subjects or with a subset of subjects assigned by random allocation before each of the treatment periods (Section 7.2.2 discusses study design in more detail), such a baseline is never used.

This baseline that collects multiple ECGs at the same time points as the ECGs will be collected while on treatment on at least one day that precedes the first administration of test is necessary for parallel studies (allows time-matched baseline).

and/or

• Immediately preceding the first dose administration of each treatment; ECGs would be collected at several time points shortly before first dose administration

such as 60 minutes, 45 minutes, 30 minutes, 15 minutes, and immediately before treatment administration.

This baseline is generally used for crossover studies and not allowed by regulators for parallel design studies.

This baseline ECG collection can be combined with the ECG collection on the day or days preceding treatment administration, resulting in complex baseline definitions and treatment difference definitions.

7.2.1.3.2 Collection of On-Treatment ECGs

The days on which ECGs are collected and the time points of collection are determined by the PK characteristics of the test drug. The intent of the study is to measure QTc at the time at which a maximum increase in QTc would occur if the drug, or relevant metabolites, does increase QTc. In crossover studies, it is often the case that the drug is sufficiently well tolerated that desired supra-therapeutic exposure could be achieved with a single dose, so only a single dose of treatments is given. Sometimes in crossover studies, it is necessary to titrate the drug up to intended exposure with multiple doses over multiple days. In parallel studies, dosing is often extended over multiple days before intended exposure is reached. For single-dose studies, ECGs are collected on the day of treatment administration at a time point shortly before the time of the maximum drug concentration (Tmax), around Tmax, and should continue even after Tmax to evaluate any delayed effects of the drug or its metabolites on cardiac repolarization. Depending on the PK of drug and metabolites, the ECG collection might continue for one or more days following the day of drug administration. For multiday dose studies, ECGs are collected according to the schedule described in the paragraph above but beginning on the day that the drug reaches steady state or intended exposure has been achieved. In some multiday dose studies, ECGs will also be collected following the first dose at identical times.

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To demonstrate assay sensitivity, ECGs should also be collected close to the Tmax of the positive control. Replicate ECGs should be collected on the same days and at the same time points in all treatment groups to ensure that blinding is maintained.

7.2.1.3.3 ECGs and Their Data on Days of Collection (Baseline and On-Treatment)

The diagrams (Figure 7-2 and Figure 7-3) below show how ECG data are organized within 10-second ECGs, and how those 10-second ECGs are organized within and across time points. Although analysis methods that use all the data from continuous monitoring over a long period (e.g., 24 hours) have been developed, the analysis usually assumes that data is organized by time points. ECGs should be recorded (or extracted from continuous recordings) in triplicate as noted above (replicates, number can vary but will generally be 3 and can be more), 30 to 120 seconds apart, to account for inherent variability; each recording lasting 10 seconds (these 10-second ECGs are either recorded as 10-second ECGs or extracted from continuous recording of the ECG record that is digitally stored for later processing, typically in 24-hour increments). Figure 7-3 illustrates the on-treatment collection of triplicate ECGs, as an example of the replicate collection, on a single day of ECG collection following treatment administration.

Figure 7-2: Illustration of 1 of 12 Leads of Continuous ECG recording from which a 10-sec ECG can be extracted Each cycle, in a normal ECG obtained from a healthy person, consists of a P-QRS-T complex and the subsequent isoelectric activity before the next P-QRS-T complex as described in Section 6.

24 hours

Beat 1 (7:59:00.00)

Beat 2 (7:59:01.30)

Beat 3 (7:59:02.15)

Beat 12 (7:59:10.05)

Beat 100,800

Extracted 10 - second ECG ≈ 12 P - QRS - T

… …

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Figure 7-3: Illustration of the concepts of recording multiple replicate ECGs at multiple time points subsequent to treatment administration (recording would also occur at baseline) ECGs are taken after subjects have rested, but not sleeping, for at least 5 to 10 minutes in the supine position (in an attempt to obtain a stable heart rate under similar physiological conditions at each time of collection). If the ECGs are to be extracted from a continuous recording, then the subjects rest as they would for actual 10-second ECG recordings.

7.2.2 Specific Designs The examples of study designs presented below illustrate specific TQT study designs. A typical TQT study is designed as double-blind (partial double-blind as in some cases the investigator might not be blind to administration of the active control), placebo- and positive-controlled to determine whether the test treatment fails to prolong the QTc (primary statistical test is for noninferiority), and to demonstrate the assay sensitivity using the positive control treatment in the study population. Traditional TQT studies employ parallel or crossover designs, generally are designed with equal study duration, and sample size for the different treatment arms or periods.

7.2.2.1 Parallel Studies

Under certain circumstances (related to the PK characteristics of the test drug), a parallel design may be preferred for a TQT study. Such circumstances include (ICH E14, 2005):

• Drugs with long elimination half-lives for which lengthy time intervals would be required to achieve steady-state and complete washout

• If carryover effects are prominent for other reasons, such as irreversible receptor binding or long-lived active metabolites

• If multiple doses of the investigational drug are required to evaluate the effect on QT/QTc intervals

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Example of TQT - Parallel Study Below is the study schema diagram for a parallel study. This study has 4 treatment arms (placebo, positive control, therapeutic study drug dose, and supratherapeutic study drug dose), which correspond to the four possible left-to-right "paths" through the study. Moxifloxacin has become the standard positive control with a well characterized (peak effect and time course) expected influence on QTc in healthy subjects with a mean increase in QTc in the range of 10 to 15 msec. Other positive control compounds are possible (e.g., low-dose ibutilide).

Figure 7-4: Parallel Study Design Schema for Example TQT Study 1 T = Therapeutic Dose (DRUG A 1 MG), ST = Supratherapeutic Dose (DRUG A 100 MG) For parallel studies, an alternative to using a separate treatment arm for active-control is to embed the active-control treatment within the placebo treatment in a blinded manner. See Section 7.2.2.3, paragraph 1 below for a reference to a published study of such design.

7.2.2.2 Crossover Studies

In comparison to parallel studies, crossover studies have at least two potential advantages: • A smaller number of subjects are typically required. Subjects serve as their own controls, resulting in

reduced variability of differences related to inter-subject variability. • Heart rate correction approaches based on individual subject data may be more feasible (as baseline ECGs

are collected before each treatment period; therefore, more ECGs are available for each subject for computation).

Example of TQT – Crossover Study Below is the study schema diagram for a crossover study. In this example, subjects were screened for eligibility and then randomized in a 1:1:1:1 ratio to receive one of four treatment sequences (Williams design). As with the parallel design, the therapeutic dose is optional. If the test drug is sufficiently well tolerated such that the necessary supratherapeutic exposure can be achieved with a single dose and washout is not lengthy, then these crossover studies often involve administration of a single dose of drug. If the drug must be titrated to reach required exposure but the titration period is not too lengthy, and washout is not lengthy, then the crossover design can be used. When the titration or washout is lengthy, the parallel design is used. Sponsors make the decision regarding whether a study should be crossover or parallel based on the required titration and or washout time. As in most crossover studies, the treatment arms are distinguished by the order of treatments, with all treatments present in each arm.

Note: Moxifloxacin is one example of a positive control.

Note: This is an optional arm.

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Figure 7-5: Crossover Study Design Schema for Example TQT Study 2

T = Therapeutic Dose (DRUG A 1 mg), ST = Supratherapeutic Dose (DRUG A 100 mg) A washout period sufficient to clear all drug exposure has to be present between treatment periods.

7.2.2.3 Non-standard Designs

A design has been used for a parallel TQT study that required lengthy treatment periods, in which the positive control treatment was embedded in the placebo treatment arm (Malik et al., 2008b). Discussion of this design alternative is beyond the scope of this white paper, but the reader can review the cited manuscript. When both a therapeutic dose and a supratherapeutic dose are studied, they might be contained in a single arm of a parallel study with the supratherapeutic dose following the therapeutic dose (dose escalation), or the supratherapeutic dose can follow the therapeutic dose (dose escalation) in a crossover study (see for example Zhang, et al., 2007). When such designs are employed, the supratherapeutic dose clearly does not have the same design characteristics as the other treatments and questions regarding potential bias can arise. Discussion of such design alternatives is beyond the scope of this white paper.

7.3 Baseline and Treatment Difference (Drug Effect) In this section, three different baseline definition alternatives are described. For each baseline definition, the resulting definition of treatment differences is described. Note that this is not an exhaustive list of possibilities. For example, triple-delta (∆∆∆QTc) treatment difference definitions are possible where both lead-in day ECGs are collected at matched time points to the time points of collection on the treatment day and one or more ECGs are collected immediately before treatment administration (and at the same time point on the lead-in days), essentially combining 7.3.1 and 0 below. Multiple lead-in days could be used to create averaged lead-in day values to be used for a time-matched baseline. Potentially, on-treatment QTc values could be compared without any baseline difference comparison, especially in crossover studies where each subject is acting as his/her own control (single-delta [∆QTc]) but we are unaware of such a single-delta ever being used for analysis in a published TQT study. Research on alternative baseline definitions is likely to continue, and what might be acceptable to any given regulatory agency at any specific point in time cannot be predicted with known accuracy. The paragraph above alludes to the potential for many alternatives and three such alternatives are discussed in greater detail below. As of the publication of ICH E-14 Q& A (ICH, 2012; Question 6), the recommended baseline for crossover studies (the most common design) is discussed in Section 7.3.3 and the recommended baseline for parallel studies is discussed in Section 7.3.1. The baseline definition discussed in Section 7.3.2 is discussed in some detail because it has received attention in published statistical literature. Again, even more alternatives can be conceptualized. The notation of the sections below has its origins in the TAUG document (see Section 5). The authors have attempted to improve upon the notation in order to make it more precise for a statistical audience. Because there is a discrepancy between the TAUG and this document at present, we hope that this discrepancy would be resolved completely in the next version.

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7.3.1 Time-Matched Lead-in Day Baseline; Double-Delta Treatment Difference For time-matched baseline, the baseline for each period is the average of the replicate set of values at a time point on the lead-in (baseline) day (Day -1) that corresponds to the post-dose time point. ECGs are collected or extracted from continuous recording in replicate sets (usually 3 replicates about a minute or so apart) at each bj and Xij. The average of the replicates is used for analysis. With the original ICH E-14 guidance, this was the standard baseline definition for both crossover and parallel studies. With the publication of the ICH E-14 Q&A (ICH, 2012; Question 6), the requirement for this baseline definition for crossover studies was relaxed (See Section 7.3.3 below).

For both parallel and crossover designs, ΔQTcij is computed for each subject at each drug (including Placebo) QTc at each time point for each day of treatment on an individual subject basis: ΔQTcij = �Xij − bj� where i=1, 2, … d, j=1, 2, … n; d=days postdose and n=time point. ΔQTcij is the change from baseline (time-matched) in QTc at each time point for each day of treatment on an individual subject basis. For crossover designs, ΔΔQTcij is computed for each subject: ΔΔQTcij = ΔQTcijDrug A − ΔQTcijPlacebo R. ΔΔQTcij is the difference between the change from baseline (time-matched) in QTc for drug and placebo at each time point for each day of treatment on an individual subject basis. For a parallel design, ΔQTcijs would be averaged across subjects: ΔΔQTcij

�� = ΔQTcij ��Drug A − ΔQTcij��Placebo

. ΔΔQTcij�� is the average difference between drug and placebo across subjects of the change from baseline (time-matched) in QTc, at each time point for each day of treatment.

7.3.2 Time-Averaged Lead-in Day Baseline; Double-Delta Treatment Difference The ECGs are collected or extracted from continuous recording in replicate sets (usually three replicates about a minute or so apart at each bj and Xij). The average of the replicates is used for analysis. The time-averaged baseline from a lead-in (baseline) day and the baseline day of each period is the average of all baseline QTc values of each of the baseline days (e.g. Day -1, 1 hour, 2 hour, 3 hour, 4 hour, etc.). Several statistical manuscripts have advocated this baseline definition over the time-matched lead-in day baseline for parallel studies (Meng et al., 2010) and both crossover and parallel studies (Sethuraman and Sun, 2009) but this has not become a regulatory standard.

For both parallel and crossover designs, ΔQTcij is computed for each subject at each drug (including Placebo) for each day of treatment on an individual subject basis: ΔQTcij = �Xij − bavg�

where bavg = ∑bj/n; i=1, 2, … d, j=1,

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2, … n; d=days postdose and n=time point. ΔQTcij is the change from baseline (time-averaged) in QTc at each time point for each day of treatment on an individual subject basis. For crossover designs, ΔΔQTcij is computed for each subject: ∆∆QTcij = ΔQTcijDrug A − ΔQTcijPlacebo R. ΔΔQTcij is the difference in the change from baseline (time-averaged) in QTc between drug and placebo at each time point for each day of treatment on an individual subject basis. For a parallel design, ΔQTcij’s would be averaged across subjects: ΔΔQTcij�� = ΔQTcij��

Drug A− ΔQTcijȷ��Placebo.

ΔΔQTcıȷ�� is the average difference in the change from baseline (time-averaged) in QTc between drug and placebo across subjects at each time point for each day of treatment. This baseline definition is discussed and arguments supporting its use are advanced in statistical literature (Meng et al., 2010 and Sethuraman and Sun, 2009) but it is not suggested as a baseline for either parallel or crossover studies in ICH E-14 Q& A (ICH, 2012; Question 6).

7.3.3 Predose Averaged Baseline; Double-Delta Treatment Difference For predose averaged baseline, ECGs are collected or extracted as replicate sets (usually three replicates about a minute or less apart) at predose in close temporal proximity to treatment administration (e.g., 15 minute intervals and immediately before treatment administration on the same day of treatment administration) and as replicate sets (usually 3 replicates about a minute or so apart) at each Xij post dose. The average of all the replicates collected predose is used as the baseline for analysis. This baseline definition has the advantage of eliminating the necessity of an inpatient lead-in day from the experimental design with all the monetary and operational expense for each treatment period. This baseline definition is now accepted for crossover studies that are both single-dose and multiple-dose administered over multiple days (ICH E14, 2012; Question 6).

For both parallel and crossover designs, ΔQTcij is computed for each subject at each drug (including Placebo) for each day of treatment on an individual subject basis: ΔQTcij = �Xij − b0�

where b0 = ∑ bj/k; i=1, 2, … d, j=1, 2, … n; d=days postdose and n=time point. ΔQTc ij is the change from baseline (time-averaged) in QTc at each time point for each day of treatment on an individual subject basis. For crossover designs, ΔΔQTcij is computed for each subject: ∆∆QTcij = ΔQTcijDrug A − ΔQTcijPlacebo where b0 = ∑bj/k; i=1, 2, … d, j=1, 2, … n; d = days postdose, k = number of predose QTc values, n = time point.

ΔΔQTcij is the difference between drug and placebo in the change from baseline (predose-matched) in QTc at each time point for each day of treatment on an individual subject basis. For a parallel design, the ΔQTcij’s would be averaged across subjects: ΔΔQTcij�� = ΔQTcij��

Drug A− ΔQTcij��

Placebo.

ΔΔQTcıȷ�� is the average difference between drug and placebo across subjects of the change from baseline (predose-averaged) in QTc at each time point for each day of treatment. Recall however that this baseline definition might not be accepted by regulators for parallel studies at this time.

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8. Analysis 8.1 Primary Analysis There are two hypothesis tests to be performed in a TQT/QTc studies:

1. The hypothesis test to confirm no study drug effect that results in a relevant prolongation of the QT/QTc as compared to the placebo group;

2. The study is capable of detecting differences in QT/QTc, (to establish the assay sensitivity) by demonstrating the QT/QTc effects of an active control that results in QTc prolongation only slightly longer than can be observed by chance with placebo (5 to 10 msec range, ideally) can be detected.

8.1.1 Testing of QT Prolongation The primary endpoint should be the time-matched mean difference between the drug and placebo after baseline adjustment at each time point. According to the ICH E14 (Section 2.2.4), the test drug is classified as negative (lack of evidence of QT/QTc prolongation), if the upper bound of the one-sided 95% confidence interval (CI) for the largest time matched mean difference between the drug and placebo excludes 10 msec. This definition was chosen to provide reasonable assurance that the mean effect of the study drug on the QT/QTc is not greater than around 5 msec. When the CI upper bound of the largest time-matched difference exceeds the threshold, the study is termed “positive” (lack of prolongation effect cannot be established) and additional ECG safety evaluation in subsequent clinical studies should be performed. The QT intervals (means of replicates) are usually measured at multiple time points to provide reasonable assurance that the mean difference between study drug and placebo on the QT/QTc interval is not greater than the pre-defined threshold. In practice, an intersection-union test (IUT) is applied to assess QT/QTc prolongation. It is the uniformly most powerful unbiased test (Berger and Hsu, 1996). The hypothesis is specified as follows:

0 drug( ) ( ): {( ) 10}, 1,2,...,i it placebo tH i nµ µ− ≥ = , versus

1 drug( ) ( ): {( ) 10}, 1,2,..., i it placebo tH i nµ µ− < =1

where drug( )itµ and )( itplaceboµ are the mean change from baseline of QT for test drug and placebo respectively, at

time point ti. The statistical model for estimating the treatment effects and the CIs depend on the study design and other factors. An analysis of covariance model (ANCOVA) or mixed effects model repeated measures (MMRM) is usually used to estimate the treatment effect and the confidence intervals. For crossover designs, the model usually includes treatment, time, period, treatment sequence, and the time-by-treatment interactions as fixed effects, and baseline as a covariate and ΔΔQTc being the dependent variable. For parallel designs, the model usually includes treatment, time, time-by-treatment interactions as fixed effects and baseline as a covariate and ΔQTc being the dependent variable. The ANCOVA model using day-averaged (time-averaged; Section 7.3.2) baseline is recommended for the analysis of parallel-group thorough QT/QTc studies (Sun et al. 2012). The baseline definition should be pre-specified (refer to Section 7.3). The authors have had success with the models specified here and are of the opinion that other covariates should only to be added if there are excellent clinical reasons for including them. Further discussion on the models and possibly the covariance structure is beyond the scope of this white paper.

8.1.1.1 Multiplicity Issues

For the test drug to placebo comparison, as noted above, an IUT method has been proposed and most frequently used as the primary method of analyzing the TQT/QTc study.

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The IUT method controls the Type I error. Specifically, the comparison between the test drug and placebo requires no adjustment for multiplicity and thus the standard one-sided 95% CIs are used at all post-dose time points. However, the IUT method may potentially lead to false positive trial results (failing to reject inferiority [not finding non-inferiority] in this specific case of TQT study analysis). The false positive rates depend on several factors including variability of the study, sample size, the number of time points, and the true mean difference to be detected. The probability of incorrectly not being able to reject a potentially clinically meaningful QT/QTc effect increases (or statistical power decreases) with the number of postdose time points (Patterson et al. 2005).

8.1.2 Assay Sensitivity The confidence in the ability of the study to detect QT/QTc prolongation can be greatly enhanced by the use of a concurrent positive control group to establish assay sensitivity. The positive control should have an effect on the mean QT/QTc interval of about 5 msec (i.e., an effect that is close to the QT/QTc effect that represents the threshold of regulatory concern, around 5 msec). However, as moxifloxacin is the accepted regulatory positive control standard, an effect in the 10 to 15 msec range for the positive control is acceptable (Florian et al., 2011). In the ICH E14 Question and Answers in 2012 (ICH E14, 2012), FDA clarified how to assess the adequacy of the positive control in the QTc study. There are two conditions required

for ensuring assay sensitivity:

1. The positive control should show a significant increase in QTc; i.e., the lower bound of the one-sided 95% CI must be above 0 msec for at least one time point. This result shows that the trial is capable of detecting an increase in QTc, a conclusion that is essential to concluding that a negative finding for the test drug is meaningful.

2. The study should be able to detect an effect of about 5 msec (the QTc threshold of regulatory concern). Therefore, the size of the effect of the positive control is of particular relevance. It determines the threshold of the lower bound. There are at least two approaches:

a. If a positive control has a known effect of greater than 5 msec (e.g., 10 msec), assay sensitivity will be

established if the lower bound of the one-sided 95% CI for the mean treatment difference between the positive control and placebo is above 5 msec for at least one time point. FDA authors (Florian 2011) have reported that for studies using oral moxifloxacin as a positive control, 18 conventional ΔΔQTcF for (moxifloxacin – placebo) ranged from 7.7 to 16.7 msec and for 11 of these studies, the range was 10.7 to 12.9 msec. This approach has proven to be useful in many regulatory cases. However, if the positive control has too large an effect, the study’s ability to detect a 5-msec QTc prolongation might be questioned.

0 active( ) ( ): {( ) 5},i ii R t placebo tH µ µ∈ − ≤1 versus

1 active( ) ( ): {( ) 5}. i ii R t placebo tH µ µ∈ − >

where R is a pre-selected subset of time points; 𝜇𝜇𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎(𝑎𝑎𝑎𝑎) and 𝜇𝜇𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝(𝑎𝑎𝑎𝑎) are mean changes from baseline of QTc for active drug and placebo respectively, at time point ti. The authors note that if moxifloxacin is used, then this criterion implicitly requires that the experimental group respond to moxifloxacin in the same manner as historical control groups. Even if the smallest ΔΔQTcF between active control (moxifloxacin) and placebo is found to be statistically significant at the conventional p<0.05 level after appropriate adjustment for multiple comparisons but

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the CI for the difference does not meet the a priori magnitude specified above, the study will be declared to have failed to demonstrate assay sensitivity. The authors recommend, based on their experience with oral moxifloxacin (see for example Loghin et al., 2013), as well as the fact that FDA authors (Florian et al., 2011) report that when oral moxifloxacin has been administered within 3 hours of a meal, mean maximum concentration (Cmax) is reduced from 3085 ng/mL to 2668 ng/mL (13.5%) due to delayed absorption and Cmax is the determinant of the maximum effect on QTc rather than total cumulative exposure (area under the curve) to reduce risk of failing to establish assay sensitivity by using intravenous moxifloxacin to avoid potential issues with other factors such as food effect.

b. If a positive control has a known effect close to 5 msec, assay sensitivity can be demonstrated if the

point estimate of the maximum mean difference with placebo is close to 5 msec for at least one time point, and the lower bound of the one-sided 95% CI for the mean treatment difference between the positive control and placebo is above 0 msec for at least one time point.

0 active( ) ( ): {( ) 0},i ii R t placebo tH µ µ∈ − ≤1 versus

1 active( ) ( ): {( ) 0}. i ii R t placebo tH µ µ∈ − >

where R is a a pre-selected subset of time points; 𝜇𝜇𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎(𝑎𝑎𝑎𝑎) and 𝜇𝜇𝑝𝑝𝑝𝑝𝑎𝑎𝑎𝑎𝑎𝑎𝑝𝑝𝑝𝑝(𝑎𝑎𝑎𝑎) are mean changes from baseline of QT for active drug and placebo respectively, at time point ti.

The analysis model of the positive control compared to placebo is similar to the analyses of the test drug compared to placebo.

8.1.2.1 Multiplicity Issues

Assay sensitivity is usually defined in terms of the statistically significant difference between the positive control and placebo at one or more postdose time points. Due to the multiple comparisons, the probability of demonstrating assay sensitivity is inflated. To avoid the inflation, the clinical trial sponsor can consider the following options:

• Perform the assay sensitivity analysis at fewer post-dose time points. Because the effect of a positive control on QTc interval is generally well understood, it is reasonable to restrict the positive control versus placebo comparisons to the number of time points when the QTc effect of the positive control is most pronounced. For example, if moxifloxacin 400 mg serves as the positive control, significant QT interval prolongation is likely to occur during the 2- to 4-hour window after the dose and the sponsor can consider excluding the post-dose ECG recordings collected after 10 hours post-dose from the assay sensitivity analysis.

• Perform a multiplicity adjustment. When performing this adjustment, it is important to utilize a multiple testing procedure that takes into account correlations among the estimated treatment differences at postdose time points (e.g., resampling-based multiplicity adjustments, Westfall and Young, 1993). Basic multiple tests such as the Bonferroni test may be avoided because they tend to be very conservative in multiplicity problems. The choice of which multiplicity adjustment method to use must be pre-specified for a specific study.

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8.1.3 Categorical Analyses Categorical (or outlier) analyses are often performed to gain an impression of the proportion of study participants who exceed predefined upper reference limit values. Outlier reference limits can be defined in terms of absolute values, change from baseline values or a combination of change from baseline and absolute value. The following thresholds are often used (but alternative limits may be used): Absolute QTc interval prolongation:

• QTc interval >450 msec



Change from baseline measurement in QTc interval:

• QTc interval increase >30 msec

• QTc interval increase >60 msec

It has to be noted that the limits above were selected based on the experience of the writers of this white paper and ICH E14 guidance. As these limits have their basis in QTcB whereas QTcF is most commonly used, it is strongly recommended for the reader to investigate recent literature from the regulators before defining their analysis (see Mason et al., 2007, demonstrating that comprable, non-parametric 98 percentile limits for 46,129 subjects with morphologically normal ECGs and no cardiac disease, was 457 msec fror QTcB and 445 msec for QTcF [12 msce for QTcF compared to QTcB]), as these recommended limits may change in the future. Change limits should be put in raw numbers or can be percentage adjusted if empirically derived percentage limits are available. All outliers should be summarized for each treatment group on at each time point and overall basis. The outlier summary tables should include counts of subjects (at each time point and overall). Therefore, if a subject experienced more than one incidence of a particular outlier event, the subject should be counted only once for that event. Many regulators might require categorical analyses of the other ECG numerical parameters as well (i.e., PR, QRS, HR). Multiple lower and upper limits exist for these additional numerical parameters and others have been suggested for QTc as well. Different regulatory bodies might have different limits of interest across time, it is strongly recommended for the reader to investigate recent literature from the regulators before defining their limits to be used in the analysis. Statistical analyses comparing treatments may be performed but is considered out of the scope of this white paper.

8.1.4 Morphological (Qualitative) Analyses Morphological (qualitative) abnormal findings (e.g., rhythm; axis; conduction; evidence of ischemia, injury, or infarction; evidence of hypertrophy; other ST abnormalities; other T-wave abnormalities; U-wave abnormalities; findings consistent with pericarditis, electrolyte abnormalities, chronic obstructive pulmonary disease [COPD], etc.) in the ECG waveform should be described and the data presented in terms of the number and percentage of subjects in each treatment arm who had changes from baseline that represented the appearance or worsening of the morphological abnormality (e.g., tables of the incidences of the observed treatment emergent abnormalities by specific abnormal finding, not just by category of findings). Special attention can be directed at abnormalities and/or changes in the appearance of the T wave/U wave that might be indicative of delayed repolarization, such as

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double humps ("notched" T wave), indistinct terminations (TU complex), delayed inscription (prolonged isoelectric ST segment), widening, flattening, and inversion. T-wave alternans (beat-to-beat variability in the amplitude, vector, and/or morphology of the T wave) is considered to be a harbinger of ventricular arrhythmias and might receive special attention with respect to occurrence of any of these findings. Several of these T-wave/U-wave findings can be numerically quantified and analyzed, but this is not a routine expectation in TQT study analyses. While the predictive value of morphological analyses is not well characterized (even if the drug does have an effect on the ECG, these abnormal morphological findings will be observed with low frequency if at all in a TQT study), differences in the incidence of abnormalities between treatment arms, if observed, could prove informative. This is particularly the case for delayed conduction (delayed depolarization) for which the TQT study would be equally sensitive as it is for delayed repolarization. Morphological changes including atrioventricular blocks (PR prolongatyion and absence of QRS complexes following P waves) and widened QRS complexes (bundle branch blocks or interventriular conduction delay) might be observed. Such delays can be observed with a variety of drugs, such as tricyclic antidepressants, and can be of equal clinical significance to that of delayed ventricualr repolarization (prolonged QTc). Statistical analyses comparing treatments may be performed but is considered out of the scope of this white paper.

8.1.5 Exploratory Analysis of Other Continuous ECG Parameters In the authors’ experience, exploratory analysis is often performed on the other continuous ECG parameters such as PR, QRS, and HR. This analysis is often performed using the same model as the one defined for the primary analysis (Section 8.1.1) to obtain estimates of the mean difference in comparison to placebo for change from baseline (no formal statistical test is usually performed only CI’s). This analysis is regarded by the authors “as good to have” but not critical.

8.2 Concentration-Response Relationship (CRR) Recent research has strongly supported the contention that intensive PK-PD modeling (CRR analysis) in ascending dose Phase I studies is sufficiently robust in demonstrating a drug’s effect or lack of effect on ventricular repolarization / QTc to substitute for (replace the need for) a TQT study (Darpo et al., 2015 and Zhang et al., 2015). As of December 2015, the ICH Working Group had expressed an official position that PK-PD (CRR) analysis might substitute for a TQT study (ICH 2015). A discussion of this topic is out of scope for this white paper. The following discussion focuses on performance of CRR analysis within a TQT study.

8.2.1 Rationale for Performing a Concentration-Response Analysis within a TQT Study 8.2.1.1 When Results of the TQT Study Are Negative (Non-inferiority Is Supported by

the Results)

When the primary analysis shows evidence of lack of meaningful QT/QTc changes, there still may be small QTc changes taking place upon administration of the investigational drug at supra-therapeutic doses below the threshold of regulatory concern. A CRR analysis can clarify whether this is the case or not and inform drug development (e.g. predict the QTc changes at doses and in subpopulations/factors that were not studied directly). It can also help in increasing confidence in regards to the time points chosen for the primary analysis, by investigating possible delayed effects. If the TQT is negative a PK-QTc analysis might not be required by authorities; however when a small drug effect is expected (based on pre-clinical info, such as human ether-a-go-go [hERG] test, animal data, etc.) it is a ”nice to have”. This is an evolving regulatory matter and regulators might want a CRR analysis even with a negative study.

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8.2.1.2 When Results of the TQT Study Are Positive (Cannot Reject Inferiority based on Study Results)

When the primary analysis does not support lack of QT/QTc prolongation, CRR analysis is an excellent tool to inform further sponsors and regulators not only about the magnitude of the possible QTc prolongation but also:

− as mentioned earlier, the primary IUT analysis is very conservative (the false-positive rate reported in literature [ICH,2014] is around 20%) and a CRR analysis can either confirm the results of the primary analysisas well as provide a potentially less-biased characterization of the drug effect than the primary analysis or strongly suggest that the results of the primary analysis represented a false-positive result and point toward further investigation being needed;

− help predict the QT effects of doses, dosing regimens, routes of administration, or formulations that were not studied directly. Interpolation within the range of concentrations studied is considered more reliable than extrapolation above the range (any conclusion drawn based on extrapolation of the observed range of concentrations are likely to be rejected by regulators);

− inform dose selection for later studies; − inform whether the QTc change occurs simultaneously with the peak concentration (Cmax) or delayed (e.g.,

effect-compartment or turnover models); − may assist and clarify the interpretation of equivocal data (on occasion, a TQT study can yield ambiguous

results). For example, if QTc prolongation is observed at a lower dose at a higher dose, or QTc prolongation is observed at a single isolated time point among a relatively large number of time ppoints, CRR analysis can help interpret the data;

− analyses of CRR by sex can be helpful for studying the effect of the drug on QT/QTc interval in cases where there is evidence or mechanistic theory for a gender difference;

− can help predict the effects of intrinsic (e.g., cytochrome P450 isoenzyme status) or extrinsic (e.g., drug-drug PK interactions) factors, possibly affecting inclusion criteria or dosing adjustments in later phase studies;

8.2.1.3 When Assay Sensitivity Is Not Demonstrated

A CCR might demonstrate that the PK-PD relationship for the positive control is as expected based on historical control and that failure to demonstrate assay sensitivity was likely due to inadequate positive control exposure due to one or more of several factors (e.g., delayed absorption of an oral formulation and failure to reach an expected Cmax due to a food effect when a meal was given shortly before the positive control). Furthermore, it might be possible to demonstrate that assay sensitivity would have likely been demonstrated if sufficient, and expected, exposure to the positive control had been achieved.

8.2.2 Methodology In all situations, it is important that the modeling assumptions, criteria for model selection, and rationale for model components be specified prior to analysis to limit bias as models with different underlying assumptions on the same data can produce discordant results. For the same reason pre-specification of model characteristics (e.g., structural model, objective criteria, goodness of fit) based on knowledge of the pharmacology is recommended whenever possible. Mixed effects model can be used to describe the CRR with (Δ)ΔQTc as response (the (Δ)ΔQTc notation will be used to show that the equation/statement applies both for the ΔQTc and ΔΔQTc subject to the study design). The following model definition can be considered: (Δ)ΔQTci (t) = Intercept(i) + drugEffect + eta(i) + eps, for subject i where eta(i) stands for subjects’ i inter-individual variability and eps stands for the residual variability.

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The drug effect is given by (i) in linear effect models drugEffect = Concentration * Slope where Slope = drug effect slope (ii) in power models

drugEffect = Concentrationb where b = drug effect power and (iii) in Emax models drugEffect = Emax * Concentration / (EC50 + Concentration) where Emax = maximal effect of the drug on QTc changes and EC50 = the concentration at which half of the maximal drug effect is reached. If a time delay is observed between peak concentration and peak QT effect (hysteresis), other models will need to be considered. These models are considered out of scope for this white paper. As with CRR analysis in other contexts, log transformation of concentration or inclusion of other parameters in the model can be considered. Further discussion on the CRR models is beyond the scope of this white paper, but they are recommended to be investigated especially in cases of a poor model fit. For crossover designs, the ΔΔQTc should be used. For parallel designs, the ΔQTc is used. There are different opinions for parallel designs whether placebo observations should be included in the analysis as having zero concentration. As no formal guidance exists at the time of writing, the authors leave it at the reader’s personal experience, but they recommend for the reader to investigate recent literature from the regulators in case such guidance is issued. The baselines recommended are the same as in the primary analysis i.e. for crossover designs “pre-dose averaged” baseline and for parallel designs “time-matched” baseline see Sections 7.3 and 8.1.1. Other considerations for CRR analysis If assay sensitivity is in question based on the results of the primary analysis, PK/PD analysis of the active control data can be performed to bring confidence in the assay sensitivity claim. The models recommended here are the same as the ones for the other PK/PD analysis. If moxifloxacin is to be used then based on Tornøe et al. (2011) and Florian et al. (2011), we recommend model (i) from the models above. Finally, the authors stress that a CRR analysis is credible only when the data are well behaved with respect to the regression line along its entire observed length.

8.3 P-Values and Confidence Intervals There has been an ongoing debate on the value or lack of value for the inclusion of p-values and/or confidence intervals in safety assessments (Crowe et al., 2009). This white paper does not attempt to resolve this debate. As noted in the Reviewer Guidance, p-values or confidence intervals can provide some evidence of the strength of the finding, but unless the trials are designed for hypothesis testing, these should be thought of as descriptive. Throughout this white paper (in the suggested tables in the last section of the document), p-values and measures of spread are included in several places. Where these are included, they should not be considered as hypothesis testing. If a company or compound team decides that these are not helpful as a tool for reviewing the data, they can be excluded from the display. Some teams may find p-values and/or confidence intervals useful to facilitate focus, but have concerns that lack of “statistical significance” provides unwarranted dismissal of a potential signal. Conversely, there are concerns that

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due to multiplicity issues, there could be over-interpretation of p-values adding potential concern for too many outcomes. Similarly, there are concerns that the lower- or upper-bound of confidence intervals will be over-interpreted. A mean change can be as high as x causing undue alarm. It is important for the users of these TFLs to be educated on these issues if p-values and/or confidence intervals are included in the TFLs.

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9. List of Outputs In TQT studies, the below list of outputs are commonly produced (for the baseline definitions for parallel and crossover studies, please refer to Section 7.3). The outputs list and shells below is mainly applicable for the parallel design (unless otherwise stated) with change from baseline (∆QTc) as the primary endpoint, for crossover design the authors believe the same list/shells are applicable by using the difference to placebo in change from baseline (∆∆QTc) being the primary endpoint instead. Furthermore, despite the list below for parallel studies including only change from baseline summary tables and plots, this is because they are regarded as an absolute necessity; many sponsors like to have these outputs repeated for the raw values. For crossovers, as stated above, the difference to placebo in change from baseline outputs are considered as an absolute necessity; however, many sponsors like to have these outputs repeated for the change from baseline and raw values as well. It is finally stressed that the list below is not an exhaustive one and only a list of commonly produced outputs. Type Title Figure Box plots of change from baseline in continuous ECG parameters by time-point for each

treatment Figure Estimated mean difference in comparison to placebo and 90% CI for change from baseline in

QTc (∆∆QTc) for treatment Figure Estimated mean difference in comparison to placebo and 90% CI for change from baseline in

QTc (∆∆QTc) for active control Figure Raw mean (+/-SE) change from baseline in continuous ECG parameters by treatment Figure Concentration response for change from baseline in QTc for active control (assay-sensitivity) Figure Concentration response for change from baseline in QTc for treatment Table Treatment comparisons of change from baseline in QTc intervals by time for treatment Table Treatment comparisons of change from baseline in QTc intervals by time for active control Table Treatment comparisons of change from baseline to all time points in ECG parameters (HR, PR,

QRS) by time for treatment Table Summary of values and changes from baseline to all time points in ECG parameters by time and

treatment Table Number and percentage of subjects meeting or exceeding clinically noteworthy QT and QTc

interval changes by time point and overall Table Number and percentage of subjects meeting or exceeding clinically noteworthy PR, QRS and

HR interval changes by time point and overall Table Number and percentage of subjects with abnormal morphological/qualitative ECG findings Listing ECG intervals (average over repeated measurements) Listing Change from baseline in ECG intervals (average over repeated measurements) Listing ECG intervals (each replicate) Listing ST segment, T-wave and U-wave morphology Listing ECG findings

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10. Outputs shells For the shells below, QTcF is often used as the example; however, if other QT correction method will be used (such as QTcI), the outputs should present that correction method instead. In summary tables/figures where only treatment and placebo are included, it is recommended that the output include a second page presenting Assay and Placebo as well (the same applies to cases where two doses of the active compound are tested). The author would like to remind the reader that the below is what the authors would expect to see for this type of study and does not prohibit anyone from providing alternative outputs. However, it has to be noted that where the authors have an understanding on what the authorities would like to see (such as the PK/PD outputs below, which originate in publications from FDA personnel) they presented them. Finally, the outputs below are not meant to overrule sponsor preferences (see for example the section on p-values above) and in particular for the listings the authors understand that the information presented will heavily rely on the raw data available.

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Figure 14.2-X.X: Box plots of change from baseline in continuous ECG parameters by time-point for each treatment

PROTOCOL/PRODUCT INFO (page x of x)

Cardiac parameter: XXXXXX

The horizontal line in the box interior represents the median. The symbol in the box interior represents the mean. Values outside the whiskers are identified with symbols. The upper (lower) edge of the box represents the 75th (25th) percentile. A whisker is drawn from the upper (lower) edge of the box to the largest (smallest)value within 1.5× interquartile range above (below) the edge of the boxPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Figure 14.2-X.X: Box plots of change from baseline in continuous ECG parameters by time-point for each treatmentAnalysis set: PD analysis set

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Figure 14.2-X.X: Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (∆∆QTc) for treatment


Cardiac parameter: XXXXXXTreatment: XXXX

PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Figure 14.2-X.X: Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (ddQTc) for treatmentAnalysis set: PD analysis set

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Figure 14.2-X.X: Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (∆∆QTc) for active control


Cardiac parameter: XXXXXXTreatment: XXXX


Figure 14.2-X.X: Estimated mean difference in comparison to placebo and 90% CI for change from baseline in QTc (ddQTc) for active controlAnalysis set: PD analysis set

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Figure 14.2-X.X: Mean (+/-SE) change from baseline in QT, QTc, and HR by treatment


Cardiac parameter:Treatment:


Raw mean (+/-SE) change from baseline in continuous ECG parameters by treatment Analysis set: PD analysis set

time (unit)

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Figure 14.2-X.X: Concentration-response for change from baseline in QTc for treatment Note: As the raw data are presented by the data points no raw data points are included in the figure. Individual points in the figure can be added but are not critical.


Compound: XXX, Matrix: YYY, Analyte:ZZZCardiac Parameter: xxx

Black line is the change from baseline in QTc vs concentration predictions and the grey band is its 90% confidence interval.Data points depict the raw change from baseline means obtained by grouping at each 10th quantile of concentrations (each subject contributes only once in the mean, if subject has more than one observation in a quantile the mean of the subject is obtained prior to the calculation).Secondary ticks depict the concentration quantiles.PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Figure 14.2-X.X: Concentration response for change from baseline in QTc for treatmentAnalysis set: PD analysis set

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Figure 14.2-X.X: Concentration-response for change from baseline in QTc for active control Note: As the raw data are presented by the data points no raw data points are included in the figure. Individual points in the figure can be added but are not critical.


Compound: XXX, Matrix: YYY, Analyte:ZZZCardiac Parameter: xxx

Black line is the change from baseline in QTc vs concentration predictions and the grey band is its 90% confidence interval.Data points depict the raw change from baseline means obtained by grouping at each 10th quantile of concentrations (each subject contributes only once in the mean, if subject has more than one observation in a quantile the mean of the subject is obtained prior to the calculation).Secondary ticks depict the concentration quantiles.PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Figure 14.2-X.X: Concentration response for change from baseline in QTc for active control Analysis set: PD analysis set

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Table 14.2-X.X: Treatment comparisons of change from baseline at all time points in QTc intervals by time for treatment Note: The model in the footnote is an example, in case of a different model the footnote needs to be updated.


Cardiac parameter:xxxxx

Scheduled time point (h) Treatment comparison N Estimate SE 90% CI p-value*x.x Drug X - Placebo xx xx xx (xxx, xxx) 0.xxxx.x x.x..x.xx.xx.x Drug X - Placebo xx xx xx (xxx, xxx) 0.xxxx.x x.x..x.x

relevant visit time period are included in the treatment comparison analysis.

Estimates are obtained from am ANCOVA model with treatment, time, time-by-treatment interactions as fixed effectsand baseline as a covariate PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Table 14.2-X.X: Treatment comparisons of change from baseline at all time points in QTc intervals by time for treatmentAnalysis set: PD analysis set

All subjects who have values at both baseline and scheduled time/

*p-value is the one-sided p-value that Estimate <10

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Table 14.2-X.X: Treatment comparisons of change from baseline in QTc intervals for active control, by time at all time points Note: The model in the footnote is an example, in case of a different model the footnote needs to be updated. The same applies to the p-value.


Cardiac parameter:xxx

Scheduled time point(h) Treatment comparison N Estimate SE 90% CI p-valuex.x Drug X - Placebo xx xx xx (xxx, xxx) 0.XXXx.x x.xx.x



Estimates are obtained from am ANCOVA model with treatment, time, time-by-treatment interactions as fixed effects

and baseline as a covariate PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Table 14.2-X.X: Treatment comparisons of change from baseline to all time points in QTc intervals by time for active controlAnalysis set: PD analysis set

*p-value is the one-sided p-value that Estimate >5

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Table 14.2-X.X: Treatment comparisons of change from baseline to all time points in ECG parameters (HR, PR, QRS) by time for treatment Note: The model in the footnote is an example, in case of a different model the footnote needs to be updated.



Scheduled time point(h) Treatment comparison N Estimate SE 90% CIx.x Drug X - Placebo xx xx xx (xxx, xxx)x.x x.x..


Estimates are obtained from am ANCOVA model with treatment, time, time-by-treatment interactions as fixed effects

and baseline as a covariate PATH DATA/PROGRAM/OUTPUT

Table 14.2-X.X: Treatment comparisons of change from baseline to all time points in ECG parameters (HR, PR, QRS) by time for treatment Analysis set: PD analysis set


*p-value is the two-sided p-value that the Estimate is not equal to 0

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Table 14.2-X.X: Summary of values and changes from baseline to all time points in ECG parameters by time and treatment Note: This table has crossover in mind, if a Parallel design, the difference between Treatment and Placebo column can be removed and only their p-value kept.

PROTOCOL/PRODUCT INFO


DayScheduled time point(h) Statistics Baseline Change p-value* Baseline Change p-value Change p-value**

XX x.x n XXX XXX XXX XXX XXXMean XXX.XX XXX.XX 0.XXX XXX.XX XXX.XX 0.XXX XXX.XX 0.XXXSD XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXMin XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXQ1 XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXMed XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXQ2 XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXMax XXX.XX XXX.XX XXX.XX XXX.XX XXX.XX

x.x n XXX XXX XXX XXX XXXMean XXX.XX XXX.XX 0.XXX XXX.XX XXX.XX 0.XXX XXX.XX 0.XXXSD XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXMin XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXQ1 XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXMed XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXQ2 XXX.XX XXX.XX XXX.XX XXX.XX XXX.XXMax XXX.XX XXX.XX XXX.XX XXX.XX XXX.XX

- Post = Post Baseline, Change= Post-Baseline - Baseline, Baselie is defined as the time-matched value on Day -1- Only subjects with values at both baseline and scheduled timepoint are included in the analysisP-value* tests if change from baseline is not equal to 0P-value** tests the change from baseline difference between treatment and placeboPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

(page x of x)Table 14.2-X.X: Summary of values and changes from baseline to all time points in ECG parameters by time and treatment

Analysis set: PD analysis set

Treatment N=xxx

PlaceboN=xxx

Difference between treatment and Placebo

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Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinically noteworthy QT and QTc interval limits by time point and overall

(page x of x)

DayScheduled time point(h) Variable

TreatmentN=xx

n/m (%)

PlaceboN=xx

n/m (%) p-value*XX x.x QTcF (ms)

Increase > 30ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXIncrease >60ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXNew > 450 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXNew > 480 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXNew > 500 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXX

QT (ms)Increase > 30ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXIncrease >60ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXNew > 450 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXNew > 480 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXNew > 500 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXX

- n : Number of subjects who meet the designated criterion- m: Number of subjects at risk for a designated change with a non missing value at baseline and postbaseline- N: Total number of subjects in the treatment group in this analysis set“new” is the number of subjects who have have an a clinically noteworth QTc at post dose which is not present at pre-dose.* P-value compares the probability of the clinically noteworthy event of active vs PlaceboPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

PROTOCOL/PRODUCT INFOTable 14.2-X.X: Number and percentage of subjects meeting or exceeding clinical noteworthy QT and QTc interval changes by timepoint and overall


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Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinically noteworthy PR, QRS and HR interval limits by time point and overall (with example limits)


Day: X

Scheduled time point(h) Variable

Treatment N=xx

n/m (%)

PlaceboN=xx

n/m (%) p-value*x.x PR increase > 25% to a value > 200 ms

xx/XX (xx.x) xx/XX (xx.x) 0.XXXQRS increase > 25% to a value > 120 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXHR decrease > 25% to a HR < 50 beats/min xx/XX (xx.x) xx/XX (xx.x) 0.XXX

HR increase > 25% to a HR > 100 beats/min xx/XX (xx.x) xx/XX (xx.x) 0.XXX

x.x PR increase > 25% to a value > 200 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXQRS increase > 25% to a value > 120 ms xx/XX (xx.x) xx/XX (xx.x) 0.XXXHR decrease > 25% to a HR < 50 beats/min xx/XX (xx.x) xx/XX (xx.x) 0.XXX

HR increase > 25% to a HR > 100 beats/min xx/XX (xx.x) xx/XX (xx.x) 0.XXX

- n : Number of subjects who meet the designated criterion- m: Number of subjects at risk for a designated change with a non missing value at baseline and postbaseline- N: Total number of subjects in the treatment group in this analysis set* P-value compares the probability of the clinically noteworthy event of active vs PlaceboPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Table 14.2-X.X: Number and percentage of subjects meeting or exceeding clinically noteworthy PR, QRS and HR interval changes by timepoint and overallAnalysis set: PD analysis set

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Table 14.2-X.X: Number and percentage of subjects with abnormal morphological/qualitative ECG findings


Day: X

Treatment Placebo

Abnormality Type Finding

New post-baseline n(%)

New post-baseline n(%) p-value

Any ECG abnormality xx (xx.x) xx (xx.x) 0.XXX

Rhythm xx (xx.x) xx (xx.x) 0.XXX Atrial Flutter xx (xx.x) xx (xx.x) 0.XXX Atrial Fibrillation xx (xx.x) xx (xx.x) 0.XXX Junctional Rhythm xx (xx.x) xx (xx.x) 0.XXX … xx (xx.x) xx (xx.x) 0.XXX

Conclusion xx (xx.x) xx (xx.x) 0.XXX complete heart block xx (xx.x) xx (xx.x) 0.XXX Left bundle branch block xx (xx.x) xx (xx.x) 0.XXX … xx (xx.x) xx (xx.x) 0.XXX

N is the number of subjects with a valid pre-dose and with at least one observation post dose and is used as the denominator in the calculation of the percentages.

“n” for baseline is the number of subjects with an ECG abnormality at least at one time point at baseline“n” for post-baseline is the number of subjects with an ECG abnormality at least at one post dose value for the Treatment columns.“new” is the number of subjects who have an abnormal ECG finding at post dose which is not present at pre-dose.A subject with multiple occurrences of an abnormality is counted only once for the corresponding treatment or for pre-dose.* P-value compares the probability of the post-dose abnormality event of active vs PlaceboPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Table 14.2-X.X: Number and percentage of subjects with abnormal morphological/qualitative ECG findingsAnalysis set: PD analysis set

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Listing 16.2.9-X.X: ECG intervals (average over repeated measurements)


Treatment / Treatment sequence: xxxx

Country/ Site/ Subject

Age/ Sex/ Race

Visit/ Day

Scheduled time point (h)

QT (ms)

QTcI (ms)

QTcF (ms)

QTcB (ms)

PR (ms)

QRS (ms)

RR (ms)

HR (beats/min)

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy x.x xx xxx xxx xxx xxx xx xx xx

x.x xx xxx xxx xxx xxx xx xx xx x.x xx xxx xxx xxx xxx xx xx xx

CNTR / ST1/ XXXXX

YY/ M/ Ca


x.x xx xxx xxx xxx xxx xx xx xx x.x xx xxx xxx xxx xxx xx xx xx

+ : QT or QTc > 450 ; ++ : QT or QTc >480 ; +++ QT or QTc > 500* : HR <50 ; ** : HR > 100^ : PR > 200@ : QRS > 120& : RR < 600 ; &&: RR > 1200! : Value has been excluded from the PD analysisECG interval values are based on the average of the repeated measurements within the same scheduled time point.Flags are applied to the average of the repeated measurements.Scheduled time includes both baseline and post baseline time points.PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

Listing 16.2.9-X.X: ECG intervals (average over repeated measurements) Analysis set: PD analysis set

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Listing 16.2.9-X.X: Change from baseline in ECG intervals (average over repeated measurements)



Age/ Sex/ Race

Visit/ Day


QT (ms)

QTcI (ms)

QTcF (ms)

QTcB (ms)

PR (ms)

QRS (ms)

RR (ms)

HR (beats/min)

CNTR / ST1/ XXXXX

YY/ M/ Ca


x.x xx xxx xxx xxx xxx xx xx xxx.x xx xxx xxx xxx xxx xx xx xx

CNTR / ST1/ XXXXX

YY/ M/ Ca


x.x xx xxx xxx xxx xxx xx xx xxx.x xx xxx xxx xxx xxx xx xx xx

ECG interval values are based on the average of the repeated measurements within the same scheduled time.PATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

PROTOCOL/PRODUCT INFO (page x of x)Listing 16.2.9-X.X: Change from baseline in ECG intervals (average over repeated measurements)


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Listing 16.2.9-X.X: ECG intervals (each replicate)

(page x of x)



Age/ Sex/ Race

Visit/ Day


ECG time (hh:mm:ss)

QT (ms)

QTcI (ms)

QTcF (ms)

QTcB (ms)

PR (ms)

QRS (ms)

RR (ms)

HR (beats/min)

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy x.x hh:mm:ss xx xxx xxx xxx xxx xx xx xx

hh:mm:ss xx xxx xxx xxx xxx xx xx xx hh:mm:ss xx xxx xxx xxx xxx xx xx xx

x.x hh:mm:ss xx xxx xxx xxx xxx xx xx xx hh:mm:ss xx xxx xxx xxx xxx xx xx xx hh:mm:ss xx xxx xxx xxx xxx xx xx xx

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy x.x hh:mm:ss xx xxx xxx xxx xxx xxx xx xxx

x.x hh:mm:ss xx xxx xxx xxx xxx xxx xx xxx x.x hh:mm:ss xx xxx xxx xxx xxx xxx xx xxx

+ : QT or QTc > 450 ; ++ : QT or QTc >480 ; +++ QT or QTc > 500* : HR <50 ; ** : HR > 100^ : PR > 200@ : QRS > 120& : RR < 600 ; &&: RR > 1200! : Value has been excluded from the PD analysisPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

PROTOCOL/PRODUCT INFOListing 16.2.9-X.X: ECG intervals (each replicate)


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Listing 16.2.9-X.X: ST segment, T-wave and U-wave morphology

(page x of x)



Age/ Sex/ Race

Visit/ Day


ECG Time (hh:mm:ss)

T-wave morphology

ST Segment

U-wave present

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy x.x hh:mm:ss xxxxx xxxx xxxxx

x.x hh:mm:ss xxxxx xxxx xxxxxx.x hh:mm:ss xxxxx xxxx xxxxx

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy x.x hh:mm:ss xxxxx xxxx xxxxx

x.x hh:mm:ss xxxxx xxxx xxxxxx.x hh:mm:ss xxxxx xxxx xxxxx

! : Value has been excluded from the PD analysisST segment: all termsT waves: all termsU waves: all termsPATH DATA/PROGRAM/OUTPUTPRODUCTION STATUS/RUN DDMMYYYY: HHMM

PROTOCOL/PRODUCT INFOListing 16.2.9-X.X: T-wave and U-wave morphology


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Listing 16.2.9-X.X: ECG findings Note: This listing will include the morphological (qualitative) abnormal findings.




Age/ Sex/ Race

Visit/ Day ECG Interpretation Comments

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy xxxxx xxxxx

xxxxx xxxxxxxxxx xxxxxxxxxx xxxxx

CNTR / ST1/ XXXXX

YY/ M/ Ca

x/ ddMMyy xxxxx xxxxx

xxxxx xxxxxxxxxx xxxxxxxxxx xxxxx


Listing 16.2.9-X.X: ECG findingsAnalysis set: PD analysis set

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11. Acknowledgements The key contributors include: Christos Stylianou, Charles Beasley, Balakrishna Hosmane, Xuewei Cui and Otilia Lillin. Additional contributors include: Charlotte Baidoo, Cathy Bezek, Greg Ball, Walter Beate, Chris Decker, Simons Gudrun, Patel Katie, Donna Kowalski, Nejamin Lang, Fang Liu, Mercy Navarro, Mary Nilsson, Palani Ravindran, John Smith, Troy Steven, Anastasia Stylianou, Sigrun Unger, Lu Zhang, and any additional contributors that may have provided comments anonymously.

12. Project Leader Contact Information Name: Christos Stylianou (Lead author for this white paper) Enterprise: ClinBAY Ltd Address: Office 401, Vanezis business center, 171 Arch. Makariou III av., Limasol,3027, Cyprus Work Phone: +35799909082 E-mail: [email protected] Name: Mary Nilsson (Analysis and Display White Papers Project Team leader) Enterprise: Eli Lilly & Company City, State ZIP: Indianapolis, IN 46285 Work Phone: 317-651-8041 E-mail: [email protected]

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mailto:[email protected]

mailto:[email protected]



13. References (ICH), International Conference on Harmonisation. "Guidance for Industry E14 Clinical Evaluation of QT/QTc

Interval Prolongation and Proarrhythmic Potential for Non-antiarrhythmic drugs Questions and Answers R1." April 5, 2012. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm073161.pdf (accessed July 25, 2014).

—. "Guidance for Industry E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-antiarrhythmic drugs Questions and Answers R2." March 21, 2014. (accessed July 25, 2014).

—. "Guidance for Industry E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-antiarrhythmic drugs Questions and Answers R3." December 10, 2015. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E14/E14_Q_As_R3__Step4.pdf (accessed December 27, 2015).

Berger, R. L., Hsu, J. C. "Bioequivalence trials, intersection-union tests and equivalence confidence sets." Statistical Science, 1996: 11(4), 283-319.

Bonnemeier, H., Wiegand, U. K., Braasch, W., Brandes, A., Richardt, G., POTRATZ, J. "Circadian profile of QT interval and QT interval variability in 172 healthy volunteers. ." Pacing and clinical electrophysiology, 2003: 26(1p2), 377-382.

Chuang-Stein C, Beltangady M. "Reporting cumulative proportion of subjects with an adverse event based on data from multiple studies." Pharmaceut Statist, 2011: 10:3-7.

Copeland KT, Checkoway H, McMichael AJ, Holbrook RH. "Bias due to misclassification in the estimation of relative risk." Am J Epidemiology, 1977: 105:488-495.

Couderc, J. P., Xiaojuan, X., Zareba, W., Moss, A. J. "Assessment of the Stability of the Individual‐Based Correction of QT Interval for Heart Rate." Annals of Noninvasive Electrocardiology, 2005: 10(1), 25-34.

Crowe B, Wang W, Nilsson ME. Advances in collating and using trial data. London: Future Science, 2014. Crowe BJ, Xia A, Berlin JA, Watson DJ, Shi H, Lin SL, et. al. "Recommendations for safety planning, data

collection, evaluation and reporting during drug, biologic and vaccine development: a report of the safety planning, evaluation, and reporting team." Clinical Trials , 2009: 6:430-440.

Darpö B., Benson C., Dota C., Ferber G., Garnett C., Green C. L., Jarugula V., Johannesen L., Keirns J., Krudys K., Liu J., Ortemann-Renon C., Riley S., Sarapa N., Smith B., Stoltz R. R., Zhou M., Stockbridge N. "Results from the IQ-CSRC prospective study support replacement of the thorough QT study by QT assessment in early clinical phase." Clinical Pharmacology & Therapeutics, 2015: 97, 326-335.

Darpö B., Sager P., MacConnell L., Cirincione B., Mitchell M., Han J., Huang W., Malloy J., Shultels C., Shen L., Porter L. "Exenatide at therapeutic and supratherapeutic concentrations does not prolong the QTc interval in healthy subjects." British Journal of Clinical Pharmacology, 2012: 75(4), 979-989.

Dmitrienko AA, Sides GD, Winters KJ, Kovacs RJ, Rebhun DM, Bloom JC, Groh W, Eisenberg PR. " Electrocardiogram reference ranges derived from a standardized clinical trial population." Drug Information Journal, 2005: 39:395-405.

F, Dessertenne. "Ventricular tachycardia with 2 variable opposing foci (translation of theFrench)." Arch Coeur Vaiss , 1966: 59:263-272.

Florian, A.F., Tornøe, C.W., Brundage, R., Parekh, A., Garnett, C.E. "Population Pharmacokinetic and Concentration-QTc Models for Moxifloxacin: Pooled Analysis of 20 Thorough QT Studies." Journal of Clinical Pharmacology, 2011: 51(8), 1152-62.

Garnett, C. E., Beasley, N., Bhattaram, V. A., Jadhav, P. R., Madabushi, R., Stockbridge, N., Gobburu, J. V. "Concentration‐QT Relationships Play a Key Role in the Evaluation of Proarrhythmic Risk During Regulatory Review." The Journal of Clinical Pharmacology, 2008: 48(1), 13-18.

Garnett, C. E., Zhu, H., Malik, M., Fossa, A. A., Zhang, J., Badilini, F., Rodriguez, I. "Methodologies to characterize the QT/corrected QT interval in the presence of drug-induced heart rate changes or other autonomic effects. ." American Heart Journal, 2012: 163:912-930.

Horowitz G, Altaie S, Boyd J, Ceriotti F, Garg U, Horn P, et al. Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline (3rd ed.). Wayne, PA: Clinical and Laboratory Standards Institute, 2008.

[Version 1.0] – [2016-March-11]



Jervell, A., Lange-Nielsen, F. " Congenital deaf-mutism, functional heart disease with prolongation of the QT interval, and sudden death. ." American heart journal, 1957: 54(1), 59-68.

Karlsson, M. O., & Sheiner, L. B. "The importance of modeling interoccasion variability in population pharmacokinetic analyses. ." Journal of pharmacokinetics and biopharmaceutics, 1993: 21(6), 735-750.

Loghin C., Haber H., Beasley C. M., Kothare P. A., Kauffman L., April J., Jin L., Allen A.J., Mitchell M.I. "Effect of atomoxetine on the QT interval in healthy CYP2D6 poor metabolizers." British Journal of Clinical Pharmacology, 2013: 75, 549-564.

Malik, M. " Problems of Heart Rate Correction in Assessment of Drug‐Induced QT Interval Prolongation. ." Journal of cardiovascular electrophysiology, 2001: 12(4), 411-420.

Malik, M. "The imprecision in heart rate correction may lead to artificial observations of drug induced QT interval changes." Pacing and clinical electrophysiology, 2002a: 25(2), 209-216.

Malik, M., Andreas, J. O., Hnatkova, K., Hoeckendorff, J., Cawello, W., Middle, M., ... Braun, M. "Thorough QT/QTc study in patients with advanced Parkinson's disease: cardiac safety of rotigotine." Clinical Pharmacology & Therapeutics, 2008a: 84(5), 595-603.

Malik, M., Färbom, P., Batchvarov, V., Hnatkova, K., & Camm, A. J. "Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval." Heart, 2002b: 87(3), 220-228.

Malik, M., Hnatkova, K., Batchvarov, V., Gang, Y., Smetana, P., Camm, A. "Sample size, power calculations, and their implications for the cost of thorough studies of drug induced QT interval prolongation." Pacing and clinical electrophysiology, 2004: 27(12), 1659-1669.

Malik, M., Hnatkova, K., Novotny, T., Schmidt, G. "Subject-specific profiles of QT/RR hysteresis." American Journal of Physiology-Heart and Circulatory Physiology, 2008b: 295(6), H2356-H2363.

Mason J.W., Ramseth D.J., Chanter D.O., Moon T.E., Goodman D.B., Mendzelevski B. "Electrocardiographic reference ranges derived from 79,743 ambulatory subjects." Journal of Electrocardiology, 2007: 40, 228-234.e8.

Meng, Z., Kringle, R., Chen, X., & Zhao, P. L. "Sample size calculation for thorough QT/QTc study considering various factors related to multiple time points." Journal of biopharmaceutical statistics, 2010: 20(3), 580-594.

Morganroth, J. "Design and conduct of the Thorough Phase I ECG trial for new bioactive drugs. In Cardiac Safety of Noncardiac Drugs." Humana Press, 2005: 205-222.

Patterson S, Agin M, Anziano R, Burgess T, Chuang-Stein C, Dmitrienko A, Ferber G,Geraldes M, Ghosh K, Menton R, Natarajan J, Offen W, Saoud J, Smith B, Suresh R, Zariffa N. "Investigating drug-induced QT and QTc prolongation in the clinic: A review of statistical design and analysis considerations. Report from the Pharmaceutical Research and Manufacturers of America QT Statistics Expert Team." Drug Information Journal., 2005: 39: 243-266.

Piotrovsky, V. "Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation." The AAPS journal, 2005: 7(3), E609-E624.

Quade d, Lachenbruch PA, Whaley FS, McClish DK, Haley RW. "Effects of misclassifications on statistical inferences in epidemiology." Am. J. Epidemiology, 1980: 111:503-513.

Rosario LA, Kropp TJ, Wilson SE, Cooper CK. "Joint FDA/PhUSE Working Groups to help harness the power of computational science." Drug Information Journal 46:, 2012: 523-524.

RT, O'Neil. "Assessment of Safety." In Biopharmaceutical statistics for drug development, by Peace KE, xx-xx. New York: Marcel Dekker, 1988.

Services, U.S. Department of Health and Human. Reviewer Guidance: Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review. 2005. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm072974.pdf.

—. Reviewer Guidance: Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review. 2005. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm072974.pdf.

—. Reviewer Guidance: Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review. 2005. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryi.

[Version 1.0] – [2016-March-11]



Sethuraman, V., & Sun, Q. "Impact of baseline ECG collection on the planning, analysis and interpretation of ‘thorough’QT trials." Pharmaceutical statistics, 2009: 8(2), 113-124.

Smirk FH, Palmer DG. "A myocardial syndrome: with particular reference to theoccurance of sudden death and of premature systoles interrupting antecedent T waves." American Journal of Cardiology, 1960: 6,620-629.

Solberg HE, Grasbeck R. "Reference Values." Adv. Clin. Chem., 1989: 27:1-79. Sun, G., Quan, H., Kringle, R., Meng, Z. "Comparison of statistical models adjusting for baseline in the analysis of

parallel-group thorough QT/QTc studies. ." Journal of biopharmaceutical statistics, 2012: 22(3), 438-462. Tornøe, C.W., Garnett, C.E., Wang, Y., Florian, J., Li, M., Gobburu, J.V. "Creation of a Knowledge Management

System for QT Analyses." The Journal of Clinical Pharmacology, 2011: 51, 1035. Westfall PH, Young SS. Resampling-based multiple testing: examples and methods for p-value adjustment. New

York: Wiley, 1993. Zhang J., Chen H., Tsong Y., Stockbridge N. "Lessons learned from hundreds of thorough QT Studies." Therapeutic

Innovation & Regulatory Science, 2015: 49, 392-397. Zhang L., Chappell J., Gonzales C. R., Small D., Knadler M. P., Callaghan J. T., Francis J. L., Desaiah D.,

Leibowitz M., Ereshefsky L., Hoelscher D., Leese P. T., Derby M. "QT effects of duloxetine at supertherapeutic doses: aplacebo and positive controlled study." Journal of Cardiovascular Pharmacology, 2007: 49, 146-153.

[Version 1.0] – [2016-March-11]

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