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Design and analysis considerations for thorough QT studies employing conventional (10 s, 12-lead) ECG recordings Expert Rev. Clin. Pharmacol. 1(6), 815–839 (2008)

Charles M Beasley Jr†, Alex Dmitrienko and Malcolm I Mitchell Author for correspondence Lilly Corporate Center, Indianapolis, IN 46285, USA Tel.: +1 317 276 4994 Fax: +1 317 276 0582 [email protected]

The QT interval from the ECG cannot be measured precisely. The relationship of the QT interval to the RR interval within individuals across time and different RR values, and across individuals eludes complete understanding. Intrinsic beat-to-beat variability in QT interval corrected for heart rate (QTc interval) is not trivial. Therefore, it is difficult to determine a valid and reliable estimate of the time for ventricular repolarization based on the QTc interval. Yet, it must be demonstrated that a drug does not result in an increase in the QTc interval that exceeds 5 ms with some reasonable degree of certainty to be quite confident that the drug does not convey some risk of ventricular tachydysrhythmia due to delayed ventricular repolarization. This demonstration can be a Herculean task due to the magnitude of variability in the QTc interval. Design features and analytical methods that might be used in the thorough QT study to improve the chances of demonstrating the true relationship between a drug and QTc interval are reviewed. Keywords : cardiac safety • drug development • International Conference on Harmonization • ICH E14• QT • QTc • QT study • thorough QT study • Torsade de Pointes • ventricular repolarization

Assessment of risk of life-threatening ventricular tachydysrhythmias due to delayed ventricular repolarization (e.g., Torsade de Pointes [TdP]) with compounds is an important component of drug development. Lengthening of the QT interval corrected for heart rate (QTc interval), representing the duration of ventricular depolarization and subsequent repolarization on a 12-lead surface ECG, is commonly used as a biomarker for an increased risk of TdP. The International Conference on Harmonization (ICH) guidance for industry document (ICH E14, Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs [101]) introduced an approach to the assessment of the potential for delayed ventricular repolarization, the thorough QT (TQT) study. The objective of this study is “to determine whether the drug has a threshold pharmacologic effect on cardiac repolarization, as detected by QT/QTc prolongation” [101] . Although ICH E14 specifies requirements for a www.expert-reviews.com

10.1586/17512433.1.6.815

‘negative study’ (one demonstrating absence of effect on ventricular polarization), the document provides little guidance on the design of a TQT study or its analysis. To understand the rationale behind, and the key features of, TQT studies, it is helpful to review important properties of the QT interval. It is difficult to measure with precision the length of the QT interval as recognized 50 years ago [1] , because the offset (end) of the QT interval (and to a lesser extent the onset) is on a curve rather than being a distinct, easily identified point. Analysis of compound-related QT interval changes is also complicated because its length depends on the heart rate. We still have a poor understanding of the precise nature of this relation­ship, not only across populations but also even within an individual [2] . Finally, inherent variability of the QT interval poses problems in its analysis although the extent of this variability may be inconsistent across populations and studies. Even with stable heart rate

© 2008 Expert Reviews Ltd

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as well as the best QT measurement techniques and QT correction methods, we continue to see a fair degree of variability over very brief time intervals. The range may be as much as 25 ms over ten consecutive complexes based on single-lead measurements [3] . We have observed up to a 40 ms range of QTc values (Bazett correction) across measurements based on automated measurement of the superimposed 12-lead median beat from 10-s ECGs collected at 1-min intervals for 30 min (30 ECGs) collected from very healthy, normal volunteers kept at rest in the supine position but awake at all times [Beasley CM Jr and Mitchell M, Unpublished Data] . On the other hand, Malik et al., while acknowledging that the observation was unexpectedly low, have recently reported daytime intrasubject standard deviation for QTc (individual correction with optimized model used for correction) of 4.93 ± 1.37 ms (the range might, therefore, have been approximately 7.6 ms) based on sophisticated measurement methods for 12-lead continuous recordings [4] . However, in this same study, the standard deviation of difference from baseline in patients treated with placebo ranged from approximately 4.2 ms to approximately 7.2 ms over 31 timepoints of ECG acquisition, across 5 days of acquisition at 2, 3, 4, 5 and 6 weeks following baseline [4] . However, the magnitude of change of interest may be as small as 5 ms (the magnitude of mean change that might be observed in the absence of drug treatment in well-measured and corrected QTc evaluations [5]). The outcome of a TQT study is classified as negative (no evidence of QTc prolongation) if the upper limit of a one-sided 95% confidence interval (CI) for the largest time-matched mean difference between the compound and placebo is below 10 ms [101] . As is pointed out in the ICH-E14: “this definition is chosen to provide reasonable assurance that the mean effect of the study drug on the QT/QTc interval is not greater than approximately 5 ms” [101] . Although not explicitly discussed in ICH-E14, the threshold for declaring absence of effect specified in the statistical analysis section of a protocol might well be indication and population specific. Decisions to continue development of a compound once TQT study results are available, regulatory approval decisions, labeling and marketability of a compound, based on the results of the TQT study reflecting the risk component of the risk–benefit ratio for the compound, might well be indication and population specific. In some circumstances (e.g., the therapeutic target for the potential medication is not considered medically serious and the intended population [e.g., children], is such that extreme safety must be reasonably demonstrated for approval) it might be advantageous to set the threshold for a negative outcome even more stringently, the upper limit of the one-sided 95% CI at less than 5 ms, for example. Conversely, if the compound was a highly effective oncolytic, targeting an advanced metastatic cancer, the upper limit of the one-sided 95% CI might be set at 20 ms. Given the definition of a negative study, probability of false-positive and false-negative outcomes can be minimized by: selecting design features (e.g., QT measurement procedures) that reduce the variability of point estimates of the treatment effect (CI around the difference between the point estimates of change for compound compared with placebo needs to be minimized) and reduce any systematic bias of point estimates of the treatment effect (e.g., if 816

the compound has no effect on ventricular repolarization, point estimates need to approach 0); and selecting analysis methods with appropriate operating characteristics (adequate power and Type I error rate controlled at a nominal level). Design features in TQT studies

Given the difficulties in QT interval measurement and analysis, it is clear that one study design will not fit all compounds in develop­ ment. This is recognized in ICH E14 [101] : “the investigational approach used for a particular drug should be individualized, depending on the pharmacodynamic, pharmacokinetic, safety characteristics of the product, as well as on its proposed clinical use”. Here, key design features that may need to be customized in a TQT study to help achieve its goals are reviewed. Homogeneous & diverse subject populations

Within acceptable regulatory bounds, it is best to select a homogeneous population of healthy subjects. By homogeneous we mean a population that displays a minimum of ‘intrinsic QTc variability’ (the variability that is observed while holding constant all things known to influence QTc), although it is difficult to offer a definition of ‘minimum of intrinsic QTc variability’ – a withinsubject characteristic. A population of patients with the condition intended as the therapeutic indication may be necessary in a TQT study if the sponsor expects safety concerns, such as with a cytotoxic compound intended as an oncolytic [101] . A homogeneous healthy subject population will help to reduce the within-subject (intrasubject) variability and, since within-subject variability is the primary determinate of the precision of the measured treatment difference (particularly in crossover studies, but can influence the variance around the estimate of difference in parallel studies), a homogeneous population will help to improve the power of the study. It would also be desirable to include a population that would be relatively homogeneous with respect to magnitude of change in QTc when exposed to an influence known to result in a change in QTc – an among-subject, population characteristic. These subject and population characteristics will increase the statistical power of a study with a fixed number of subject participants. It is quite important to exclude subjects who show a systematic change in QTc due to factors other than exposure to drug where exposure to those factors cannot be controlled and balanced within the conduct of the study. Such subjects will potentially have an adverse influence on the point estimate of treatment differences and contribute bias that would increase the potential for either false-negative or -positive results. General exclusion criteria

To achieve a more homogeneous subject population, factors need to be removed that directly contribute: • Higher degrees of inherent QTc variability • Higher risk of QTc prolongation due to factors other than compound exposure • Increase the variability of compound exposure Expert Rev. Clin. Pharmacol. 1(6), (2008)

Design & analysis considerations for thorough QT studies

For example, the following exclusion criteria can be considered in a TQT study: • Subjects with personal or family history of long QT syndrome, heart failure or hypokalemia • Subjects with ECG abnormalities, including abnormal QTc interval and conduction abnormalities that may affect QTc analysis • Subjects who smoke • Subjects with larger BMI values Any factor that would degrade precision in measurement of QT or cause it to be abnormal (i.e., U waves [or other T-wave ‘obscuring features’], bundle branch block or interventricular conduction delay) should be exclusionary. Observation of extreme variability in heart rate at rest (which might confound the ascertainment of an intrasubject RR/QT relationship) might be considered a reason for exclusion. Other exclusion criteria might be required to protect individual patient safety and should be carefully attended to, based on the active comparator and the test-drug characteristics. Gender-specific exclusion criteria

The study population can be restricted to males in order to increase homogeneity; however, this is a complex issue. Several matters bear on the decision to include or exclude females from TQT studies. Females have longer QT intervals than males but this is not a reason to require that females be included in TQT studies. QTc duration at baseline is not known to be a factor influencing the likelihood of experiencing QTc prolongation or the magnitude of QTc prolongation when exposed to a QTc-prolonging drug. Although a comparable magnitude of QTc prolongation, relative to males, from a greater baseline value might place females at a greater risk of TdP (because the absolute magnitude of QTc interval influences the degree of TdP risk), this is not a reason to include females. A TQT study evaluates magnitude of QTc changes rather than the incidence of TdP. Females are more likely to develop TdP when experiencing a predisposing factor for TdP relative to males. As with the difference in baseline QTc length, this is not a reason to include females. Premenopausal females may add more complexity in that their QT variability may be linked to the phases of their menstrual cycle and/or interaction of the phase of menstrual cycle with autonomic tone [6–9] . Although these data are not completely consistent, when blockade of sympathetic and parasympathetic activity is accomplished, the relationship between this variability and the phase of the menstrual cycle appears clear [6] . Even more importantly, the response to rectifier potassium current (Ikr) blockers, with respect to QTc length, may change in females across phases of the menstrual cycle [10] . Such systematic, temporally linked within-subject variability in QTc interval is a strong reason against the inclusion of female subjects. This systematic change would introduce variability and would potentially create bias unless appropriate controls were introduced. These controls include the requirement that each female begin each study treatment at the same point within her individual menstrual cycle (in a crossover study) and a matching of www.expert-reviews.com

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females for points in their menstrual cycles across treatment arms (in a parallel study). Menstrual cycles of different lengths would further complicate the matter. Note also that, not only might there be systematic change in basal QTc interval with the menstrual cycle, but if females do show greater sensitivity to QTc prolongation (discussed later), this degree of differential sensitivity, relative to males, may fluctuate over the course of the menstrual cycle. Females might or might not display greater QTc prolongation when exposed to a QTc-prolonging compound relative to males at the same exposure level (e.g., identical maximum value and AUC of plasma compound concentration). If females are more sensitive to the effects of compounds that delay or prolong ventricular repolarization than men, then this gender difference argues for their inclusion, lest a positive finding be possibly missed in a relevant target population. QT/QTc assessment data suggest that females might show a greater increase in QTc interval than males at a given concentration of a QT prolonging agent [11–19] . However, in summary it is important to note that: • The data are not consistent • Pure Ik r blockers may not show this difference across genders • The difference might be accounted for more by changes in QRS length than JT length (end of QRS to end of T-wave representing actual ventricular repolarization) • The magnitude of this difference is small and might be in the order of what might be observed within females with changes in phase of the menstrual cycle If there is a difference in the magnitude of QTc changes associated with known QT-prolonging agents between males and females, it differs at various stages of the menstrual cycle and ranges from 15 to 44% [10,11] . For example, if a compound were to prolong the QTc interval in a male by 10 ms, it might prolong QTc in a female by 11.5–14.4 ms, provided there is differential prolongation and depending on the point in the menstrual cycle. Thus, QTc changes associated with the phase of the menstrual cycle might well be greater than QTc changes that would be considered of potential clinical significance in a TQT study. On balance, inclusion of females in a TQT study would probably increase undesirable variability. With respect to inclusion of females in TQT studies there are two opposing considerations: • It is probable that females are more sensitive to QTc-prolonging effects of drugs that prolong QTc through blockade of Ik r channels – inclusion of females (exclusively females) would increase assay sensitivity and be desirable; • It is probable that females experience systematic changes in QTc associated with the time course of the menstrual cycle – inclusion of females could result in systematic bias of the point estimate in treatment difference and this bias would not be desirable. As an alternative to including females, with a possible increase in the sensitivity of the assay but with the accompanying possi­ bility of introduction of bias, increasing the dose of the compound would serve to increase the sensitivity of the study. Any increased 817

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sensitivity of females is probably due to a decreased tissue concentration of the cardiac ion channels that, when blocked by a given concentration of the compound, lead to a delay in ventricular repolarization and an increase in QTc interval. This matter requires careful consideration of the alternatives and will probably be a subject of regulatory discussions if a study design excludes females but, in the authors’ opinion, sound reasons exist for excluding females, without compromising the sensitivity of the study. Genetic screening

Finally, to achieve a homogeneous subject population, the trial sponsor can theoretically consider genetic screening, as mentioned in ICH E14 [101] ; however, this is a very expensive option. In general, it is recommended that the trial sponsor work with the regulatory agency that will be reviewing the study protocol to define an acceptable subject population consistent with the compound’s indication. Parallel & crossover designs

The choice of the study design (parallel vs crossover) is straightforward. This decision is driven by pharmacokinetic properties of the compound. A crossover design is recommended unless the required washout period is excessively long. It may be difficult to retain subjects in a crossover design with three treatment periods when the washout period is longer than 3 weeks. The length of each treatment period is also an important consideration since it contributes to the length of the study. When a crossover design is employed in a TQT study, it is important to ensure that a balanced Williams design is used [20] . In a Williams design, each treatment precedes every other treatment, excluding itself, equally often and thus a balance for carry-over effects is achieved. To maintain the blinding of the compound and placebo, the actual treatment randomization sequences should not be defined in the study protocol. While unbalanced/incomplete crossover designs can also be considered (e.g., placebo as the first treatment in all sequences) and would translate into less time per individual subject and less total time for the study (e.g., one washout period can be eliminated when placebo is the first-treatment arm), such designs are not recommended. Such unbalanced designs would not compensate for potential, systematic period effects or carry-over effects. If a parallel design must be used, less time commitment from subjects will be required. However, since the analysis of QTc effects in parallel studies relies on between-subject comparisons (rather than within-subject comparisons in crossover studies), the sample size in a parallel study will be considerably larger compared with a crossover study with similar characteristics. As shown in Malik et al. [21] , Zhang and Smith [22] , and Zhang, Dmitrienko and Luta [23] , the sample size in TQT studies with a parallel design can exceed 100 subjects per group, whereas the total size in TQT studies with a crossover design is typically fewer than 100 subjects. Lastly, it is also worth noting that hybrid designs have been discussed in the literature, where studies have several crossover groups that are run in parallel [21] . 818

Number of dose regimens

The choice of the number of dose regimens and dose amounts in a TQT study is driven by several considerations. Although ICH E14 does not require that multiple dose levels be evaluated in a TQT study, two dose levels (a therapeutic dose and a supratherapeutic dose) are often included in TQT studies [24–26] . This can be done to better understand the dose–response relationship (it normally takes more than two doses to adequately characterize a dose–response function) if any such relationship exists. In addition, the therapeutic dose can serve as a fallback strategy when there is evidence of QTc prolongation at the supratherapeutic dose. If the supratherapeutic dose prolongs the QTc and a therapeutic dose has not been studied, it cannot be assumed by either the sponsor or the regulatory authorities that a therapeutic dose would not also produce a relevant QTc increase. If in the case of prolongation with a supratherapeutic dose, the maximum therapeutic dose does not produce a QTc prolongation, then the drug candidate might be viewed less negatively by regulatory authorities. With data demonstrating absence of prolongation at a maximum therapeutic dose, the prolongation at a supratherapeutic dose, under the assumption that the magnitude of prolongation in the context of the drug’s benefits does not result in a nonapproval, might be placed in a more positive context in product labeling by describing the effect at both the supratherapeutic dose and the maximum therapeutic dose. If the compound is unlikely to have a QTc-prolongation effect, it is difficult to justify (from an efficiency perspective) an additional, lower dose level (therapeutic dose) in a TQT study. The supratherapeutic dose level is intended to achieve the highest exposure levels anticipated in clinical practice (e.g., exposure levels caused by drug–drug interactions and/or genetic poly­ morphisms affecting drug metabolism). ICH E14 [101] states ‘if not precluded by considerations of safety or tolerability due to adverse effects, the drug should be tested at substantial multiples of the anticipated maximum therapeutic exposure.’ in order to achieve the goal of highest possible expected exposure (other than, perhaps, through overdose). Lack of tolerability of such a dose might require reduction of this desired supratherapeutic dose. Single-dose versus titration to steady-state designs

Several options are available when achieving a supratherapeutic exposure. Administering the compound as a single dose is one approach [27] . This approach is generally recommended for welltolerated drugs with no active metabolites. This method has the advantage of minimizing the time between any baseline measurement and on-compound measurement. As this time lengthens, the potential for systematic, intrasubject shifts in QTc interval may increase. Multiple administrations of a fixed dose of the compound can be used to achieve a steady state level [25] . This approach is relevant in the presence of one or more metabolites and/or when the drug is poorly tolerated. Combining evaluation of steady state QT effects with determination of a tolerable dose is a method that has been employed. The compound is uptitrated with steady state achieved with each Expert Rev. Clin. Pharmacol. 1(6), (2008)

Design & analysis considerations for thorough QT studies

dose titration, and ECG data are collected at multiple doses during the uptitration [28] . This approach is more suitable when tolerability at higher doses is unknown. This design has the inherent dis­advantage of being an unbalanced design (a lower dose arm always precedes a higher dose arm) but may be a practical necessity with certain poorly tolerated drugs. The alternative with poorly tolerated drugs, or drugs otherwise requiring titration and where it is desirable to study several doses, would be uptitration with evaluation only at one specific dose level. Each dose of interest would be studied in a separate arm (either crossover or parallel). Another method that would provide some degree of additional balance over collection at multiple uptitration doses would be to both titrate up and titrate down, collecting ECGs on the uptitration for some subjects and on the downtitration for other subjects. This method would not allow for complete balance. An additional drug can be administered that inhibits the metabolism of the compound (if the compound is metabolized through an enzymatic [typically a cytochrome P450 (CYP) enzyme] pathway for which an inhibitor is available for human administration). This method is described in ICH E14 [101] . We strongly recommend against use of a metabolic inhibitor. Many drugs that do inhibit various cytochrome P450 enzymes are known to have an influence on ventricular repolarization and their administration would, therefore, confound the observed results of the study [29] . Although some metabolic inhibitors are not known to prolong QTc (and even believed to not prolong QTc), without the demonstration of this absence of effect within an actual TQT study, with a point estimate of effect being essentially 0, there would be some risk of confounding of study results with another drug. Ritonavir, a CYP3A4 inhibitor, has been confirmed to have essentially no effect on QTc in a TQT study [30] but as a protease inhibitor it carries a high risk of nausea and frank vomiting (among other adverse effects) that might well have an indirect influence on the QT interval if they were to occur, compromising the experiment. Subjects genetically predisposed to poor metabolism of the compound due to genetic deficiency of an enzyme in the metabolic pathway for the compound can be used. Positive control

A positive control is included in TQT studies to assess the sensitivity of the study in detecting drug-related QT effects [101] . The assay sensitivity is established if the control is shown to induce QTc prolongation. A positive control is generally expected to have a moderate effect on QTc interval, in the range of 5–10 ms (greater than the effect observed with placebo), and this effect is expected to be statistically significantly different from the effect of placebo (see later for a detailed discussion of statistical criteria used in the assay sensitivity analysis). Orally administered moxifloxacin 400 mg is currently the most commonly used positive control. It has been used in the majority of TQT studies because its effect on QTc interval has been well characterized [31] . The peak plasma concentration for a 400 mg dose of moxifloxacin occurs 2 h after the dose and the maximum www.expert-reviews.com

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QTc effect is achieved slightly after 2 h postdose. As reported in recently published summaries of TQT studies, the mean maximum effect of this positive control is variable and ranges between 6 and 10 ms but can exceed 10 ms [26] . Control of QT variability

This can be accomplished by controlling/removing known sources of QT variability and lowering the background QT measurement variability via signal averaging. Controlled environment

To remove known sources of QT variability, steps need to be taken to ensure that the relationship between all temporal activity (eating, drinking, sleeping and exercise timing) and the ECG collection schedule be identical within subjects (across treatment periods or arms) and across subjects. ECG recordings need to be taken in a quiet controlled environ­ ment (restrict audio/visual entertainment and video game playing). It is advisable that subjects rest in a supine position for approximately 10  min prior to ECG recordings. Additional factors that need to be taken into account include food intake. Consumption of a meal has been shown to lengthen QTc interval [32] . To prevent the confounding effect of meal consumption, subjects may be required to fast overnight prior to receiving the compound and continue fasting until after the pharmacokinetic tmax (the timepoint associated with the peak QTc effect, if any) has been achieved in single-dose administration studies. Meal consumption should be standardized relative to all ECG recordings (ideally at least 2 h prior to recordings) and be held constant across treatments within subjects and across subjects as well. Measuring the QT/QTc interval

Measurement of QT/QTc is a complex topic involving several subtopics. In general, TQT studies will have the ECG data collected by digital systems. These data will go through some degree of computer preprocessing prior to representation of waveforms in an electronic format (usually with computer-suggested measurements) for final measurement or ‘over-read’ by a human (technician and then cardiologist or even more complex process). A study could be based entirely on the computer measurements but this is discouraged by ICH E14 [101] . Multiple vendors offer the ECG collection devices and the dedicated hardware (and software/algorithms) that preprocess and display the data, as well as performing measurements. Final measurements by humans will be constrained and influenced by the characteristics of the hardware and software of the systems selected by the sponsor. Vendors are evolving both the hardware and software. Given a specific vendor’s systems, there are several additional aspects of the measurement process that could be selected by the sponsor that might influence bias in the point estimate of difference between treatments and variability in this estimate. The lead (or superimposition of beats or derived median beats from leads) selected for measurement is one such aspect measurement discussed by Malik and Camm [3] . Several methods are available that can be applied by computer or human in the effort to 819

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Figure 1. Relationship between the sample size to achieve 95% power in a crossover design and number of replicate ECG recordings at each schedule timepoint (mean treatment difference is 5 ms). The solid line represents the sample size corresponding to an infinite number of replicate ECGs and the dashed line represents that sample size increased by 30%.

establish the end of the T-wave and these might be specified by the sponsor for the human over-read process. Finally, the sponsor can establish various methods to attempt to improve precision and reliability of measurements. Malik et al. have recently described highly complex and intensive measurement and validation of measurement methods utilizing both computer and human processing that may well result in decreased variance and improved point estimate precision [4] . Detailed discussion of these various aspects of the measurement process is beyond the scope of this review.

beats from multiple leads are used for measurement purposes for derivation of a single value for a 10-s ECG, multiple beats have already contributed to the ‘signal averaging’ process prior to averaging the values for the multiple 10-s ECGs. Zhang, Dmitrienko and Luta have discussed this influence of replicate number on sample size requirements for a given power based on study data observations for specific populations using specific definitions of treatment difference [23] . Figure 1 depicts the relationship between the number of replicates and required sample size in a study designed to have 95% power when the true mean treatment difference is 5 ms. With an infinite number of replicate ECGs, 24 subjects are required to achieve the desired power level. The authors suggest that a reasonable compromise between subject number and replicate number is to increase the sample size over that required when using an infinite number of replicates by 30% (in the example, a total of 32 subjects) and using the number of replicates corresponding to this sample size. In the case of this example, that is four replicates. Note that the design with four replicates provides a considerable reduction (54%) in the required sample size compared with the design with a single replicate. Similar methods can be used to determine the number of replicate ECGs for other treatment difference definitions and subject populations. The calculations and results earlier emphasize the precision of treatment effect estimates. By reducing the background QT measurement variability, the sponsor can also ensure that the observed estimates of the treatment difference are close to their true values (e.g., the estimates should be near 0 when a placebo is compared with a placebo in a TQT study). The bias reduction considerations can therefore guide the selection of the number of replicate ECG recordings. Lastly, when selecting the number of replicates, it is worth keeping in mind that, as the length of the period within which replicate ECG recordings are collected extends, it becomes more difficult to control other factors influencing QT intervals.

Signal averaging

Timepoints of ECG acquisition & values for comparison derived from data (individual QTc values) collected at these timepoints

Signal averaging is used in TQT studies to strengthen the signal by averaging QTc values collected in close temporal proximity, generally in the range of every 30 s to 2 min, around each point of ECG acquisition. The number of replicate ECG recordings at each timepoint directly influences the precision of the results and the power of a TQT study (the sample size required to achieve a specified power level). In recent TQT studies, this number ranged between three and ten [26–28,33] . The incremental improvement in the precision of measuring QT interval beyond six to seven replicates is very small, thus, three to six replicates are generally recommended for signal averaging purposes in TQT studies. The larger number of replicates is advised when a more stringent test of noninferiority is established based on the upper bound of the one-sided 95% CI being less than the conventional 10 ms. It should be noted that when several beats from a single lead are averaged, or median beats or superimposed median

The number and timing of ECG recordings taken after compound administration are driven by the pharmacokinetic and pharmacodynamic properties of the compound. The timepoints for ECG acquisition should be chosen based on the expected time course of compound-related QTc changes (if any occur), for example before, around and after the pharmacodynamic tmax. However, the pharmaco­dynamic tmax is difficult to predict precisely and does not exist if there is no QTc effect. Pharmacokinetic parameters (e.g., the pharmacokinetic tmax) are therefore used to determine the number and timing of postdose ECG recordings. Without major metabolites, significant interindividual variability in pharmacokinetic tmax, and hysteresis (for compounds believed to have an effect), at least four postdose ECG recording timepoints need to be considered to adequately characterize the time course of QTc changes:

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Design & analysis considerations for thorough QT studies

• Before the pharmacokinetic tmax • Around tmax • ‘Slightly’ after tmax • At an anticipated trough point The timepoints around tmax must account for both individual differences in pharmacokinetics and hysteresis. If the pharmaco­ kinetic tmax tends to be highly variable, additional postdose ECG recordings are generally included around the pharmacokinetic tmax. If the compound does produce an effect on the QTc interval, hysteresis is a possibility and the specific characteristics of the hysteresis effect for QTc on an individual basis may be sufficiently difficult to predict, such that predictions might not allow reduction in the number of timepoints of ECG acquisition required by regulatory authorities. The possibility of an effect on QTc interval and hysteresis suggest the possible need for additional recordings following the pharmacokinetic tmax. In addition, when metabolites that might influence the QTc interval are present, ECGs also need to be taken around the metabolite-specific tmax points. The more postdose ECG recordings are taken the better the time course and magnitude of any QTc effect will be estimated. However, as explained later, the multiplicity burden increases with the number of postdose ECGs (i.e., including too many postdose ECGs increases the likelihood of incorrectly failing to conclude that the compound does not induce QTc prolongation and reduces the statistical power of the study). Study design needs to find a balance between the requirement to characterize accurately any QTc changes and the study power. In addition to simply comparing treatment differences in QTc (or some change in QTc) at each individual timepoint of ECG acquisition, there are specific timepoints (one timepoint that is constant across all subjects) of acquisition of QTc values that can be of particular interest for comparison: • Timepoint of maximum difference in the means (maximum mean difference – basis for ICH E14 definition of primary value of interest) • Time point of maximum mean on compound • Value at the timepoint (tmax) associated with the Cmax that has been previously established The second of these timepoints of interest might well not be associated with the largest difference from placebo and placebo control/comparison is probably quite important. The timepoint of average maximum concentration clearly represents a central tendency estimate and use of timepoints of actual peak plasma concentration on an individual basis (see next paragraph) will provide a better estimate of differences associate with peak plasma concentration. Furthermore, there are other QTc values that can be derived from those collected at each specific timepoint that can be compared: • The mean of all post-administration QTc values; www.expert-reviews.com

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• The mean of the maximum QTc value difference (maximum difference at any of the timepoints) for each subject (timepoint can be different for each subject; mean maximum difference; contrasts with the maximum mean difference [first point in previous list]); • The mean of the difference for the timepoint of maximum QTc value on compound for each subject (timepoint can be different for each subject); • The mean of QTc values from the timepoint associated with maximum plasma concentration of compound for each individual subject (time-matched values during placebo would be used for the comparison). The first of these derived values might mask a real effect of interest if that effect is relatively brief, although it might serve as an approximation of an adjustment for multiple comparisons. The second derived value is clearly biased against a finding of noninferiority as it averages all maximum differences where the parameter of comparison is subject to substantial variability. The third derived value fails to give weight to the placebo value. The fourth derived value, while considering peak plasma concentration, does not consider possible hysteresis. These various possible alternative values for comparison is a concept linked to when ECGs are collected but also intimately linked to the concept of the definition of treatment difference, which is discussed later. Analysis methods in TQT studies Correction of QT interval for heart rate Historical population QT correction methods

The first two QT correction formulas were proposed by Bazett [34] and Fridericia [35] : • QTc = QT/√RR (Bazett square-root correction) • QTc = QT/3√RR (Fridericia cube-root correction) These and other QT correction formulas proposed in the literature (e.g., Framingham formula [36] ) are historical population QT corrections. They perform well only if restrictive conditions are satisfied, for example, the QT–RR relationship for all subjects is close to the one assumed by the correction formula, heart rate does not change over time. If a compound effects the heart rate, the corrections become unreliable and underestimate or overestimate the true QTc effect [2,4,20,37,38] . The Fridericia QT correction tends to perform better than the Bazett correction in this case but it still needs to be used with caution. Despite this limitation, such corrections are generally required to be included in TQT studies. ICH E14 [101] emphasizes that “QT interval data corrected using Bazett’s and Fridericia’s corrections should be submitted in all applications, in addition to QT interval data corrected using any other formulae.” Since the Bazett QT correction is considered less reliable than the Fridericia QT correction, there has been a trend to put less emphasis on the former in TQT studies. 821

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Data-driven QT correction methods

Data-driven corrections are based on the specific population included in a study. Hollister and Montague recommended that data-driven QT correction methods, including study population-, individual- and model-based corrections, be used in the analysis of QT data in regular clinical studies and TQT studies [39] . Study population QT correction method

The most basic form of a data-driven QT correction method is a population-based QT correction specific to an individual study. A study population QT correction formula is computed from drug-free (off-treatment) ECG recordings. A linear or log-linear regression model (other models could be used as well) is fitted to pooled off-treatment ECG recordings, • QT = a + bRR (linear regression) • logQT = c + d logRR (log-linear regression) and the estimated slopes (b or d) are used to define linear or log-linear QT correction formulas • QTc = QT + b(1-RR) (linear population QT correction) • QTc = QT/RRd (log-linear population QT correction) Such population QT corrections rely on the assumption of a constant QT—RR relationship across subjects and timepoints within each subject [39] and perform poorly if the conditions are not met. Individual QT correction method

There is ample evidence that the QT–RR relationship varies substantially from individual to individual [2,5,21] and thus individual (subject-specific) QT correction formulas are likely to improve the assessment of QTc. The individual QT correction method is recommended as “most suitable for the ‘thorough QT/QTc study’ and early clinical studies” in ICH E14 [101] . Within the individual correction framework, a QT correction formula is derived for each subject using his or her off-active treatment ECG recordings. Both linear and log-linear regression models (as well as other models) can be used to calculate individual QT corrections. This process is similar to the one used in the derivation of population-based QT corrections described earlier. The approach described earlier uses a single QT–RR regression model to derive individual QT correction formulas for all subjects. An individual correction method can be used that selects the best regression model for each subject from several candidate models. Malik et al. utilized this method to calculate individual QT corrections based on the best subject-specific models selected from 12 candidate models [2,21] . This subjectspecific model individual QT correction method led to an improvement compared with the standard correction method (in terms of the precision of an estimated treatment effect and required sample size). A sufficiently large number of ECG recordings per subject are required to compute reliable individual QT correction formulas. Hollister and Montague gave the following rule of thumb, 822

“20–50 off-treatment observations per subject are needed for use of an individual correction, with more being better” [39] . The actual number of recordings depends on a number of factors, including the range of heart rate values for each subject. If QT variability is low, the range of heart rates is wide, and heart rate values are distributed evenly over this range, a smaller number of recordings may be sufficient. A model-based approach might be applied to facilitate the estimation of individual QT–RR relationships and derivation of individual QT corrections with a limited number of ECG recordings per subject [40] . Model-based QT correction method

Statistical modeling might improve the performance of QT correction methods by taking into account the correlations among repeated ECG recordings for each subject and heart rate changes. For example, the model-based QT correction method proposed by Dmitrienko and Smith relies on modeling changes in QT interval as a function of changes in RR interval to determine a heart rate-independent estimate of the treatment difference (this estimate corresponds to the zero change in RR interval) [38] . This QT correction method enables a reliable analysis of QT changes independent of RR changes when heart rate changes occur. In addition, the method performs as well as (and sometimes better than) population QT corrections when no heart rate effect is present. Other model-based approaches to analyzing the QT–RR relationship were discussed by Shah and Hajian [41] . For either the correction computation approach (derive a formula and compute a QTc) or the model-based QT correction method to be valid, one needs to assume that as the RR range is shifted the shape of the QT–RR relationship remains constant. It is generally accepted that the constancy of this relationship holds true, at least with a relatively stable heart rate and without significant alterations in the autonomic tone. However, it is theoretically possible that this relationship can be altered by the compound (or other factors) and there is some evidence to support the possibility of a change over time [42,43] . If this does occur, it may be impossible to conduct a valid TQT study using either correction approach discussed previously. Observed QTc changes may become an artifact of changes in the slope of the QT–RR relationship and lose their clinical relevance. Comparison of compound & positive control with placebo Twofold objective

TQT studies are designed to evaluate QTc effects of a compound, placebo and a positive control. There are two objectives of TQT studies: • Demonstrate that the compound does not prolong QTc interval compared with placebo (noninferiority test); • Demonstrate that the study is capable of detecting prolongation of the QTc interval by a positive control compared with placebo (classical superiority test). This is an assay sensitivity analysis. Patterson et al. pointed out that other comparisons can be performed in TQT studies, for example, a direct comparison between the compound and the positive control [44] ; however, Expert Rev. Clin. Pharmacol. 1(6), (2008)

Design & analysis considerations for thorough QT studies

it is uncommon to do this. A comparison of this type was performed in the tadalafil TQT study [27] to demonstrate that the magnitude of QTc prolongation for the compound was significantly lower than for the positive control (a ‘secondary’ test of lack of effect). As described earlier, the analysis corresponding to the first objective is clearly defined in ICH E14 [101] . The compound is said to lack a QTc prolongation potential (be noninferior to placebo) if the upper bound of the one-sided 95% CI for the largest time-matched mean difference between the drug and placebo excludes 10 ms. The second objective is defined in a more ambiguous manner. ICH E14 states that assay sensitivity will be established if the mean difference between the positive control and placebo is close to 5  ms [101] . Two definitions for this objective have been proposed. Assay sensitivity will be established if the lower bound of the one-sided 95% CI for the mean difference between the positive control and placebo is greater than a prespecified threshold, such as 5 ms at one or more postdose timepoints. This criterion, by itself, does not appear to be consistent with the ICH E14 statement regarding the requirement for demonstrating assay sensitivity. With this criterion, it is easy to demonstrate assay sensitivity simply by selecting a positive control with a large QTc effect, which will defeat the purpose of including a positive control in a TQT study [29] . An alternative definition emphasizes concern over excessively large QTc effects on the part of the positive control. Assay sensitivity is defined as the requirement to show that the positive control is statistically different from placebo at one or more postdose timepoints when the mean difference between the positive control and placebo is close to 5 ms (60 ms increase) or above a certain threshold (e.g., absolute value > 500 ms) might be more predictive of TdP risk. However, as with the mean increase data, relationships between incidence of these categorical changes and TdP risk have not been established (with a given number of subjects, there is generally less statistical power when statistical tests are based on such categorical comparisons). For obvious ethical reasons, studies to establish the relationship between magnitude of mean increase (or categorical change) and risk of TdP, which would allow application of receiver operating characteristic analysis to determine an optimized threshold of concern, will not be performed. Changes in T-wave morphology may be more predictive of TdP than increases in QTc interval length. Active research seeks to determine these characteristics and specify the relationships between specific changes and degree of risk. As with QTc interval measurement, it remains to be seen as to the extent to which such T-wave characteristics, if identified, can be ascertained with precision and the extent of variability in the observations of these characteristics. Given the historical entrenchment of QTc interval length as the biomarker for TdP risk that is readily accessible in humans, regulatory authorities may be slow to move away from the TQT study paradigm even if more predictive biomarkers are tentatively established. The evidence supporting primarily the negative predictive power of the absence of the biomarker would probably need to be substantial for such a biomarker. It is undoubtable that additional advances will be made in the preclinical prediction of the presence or absence of risk of TdP with drug candidates. However, many regulatory authorities demand human assessment of critical, life-threatening safety matters, even when methods applicable to humans may be less predictive than the preclinical methods. In the current regulatory and public expectation environments, particularly in the USA, human studies of TdP risk will undoubtedly continue to be required into the near future. Expert commentary

As reflected in the Key issues and the 5-year view, the study of methods to determine the risk of TdP with a potential new drug is a rapidly evolving area in dire need of such evolution. Progress is likely to be made in methods that will allow better use of the corrected QT interval as a biomarker of risk: better measurement; Expert Rev. Clin. Pharmacol. 1(6), (2008)

Design & analysis considerations for thorough QT studies

better correction (including correction for both variability and change in RR); and a better understanding of currently observed variability in the QTc that will allow for appropriate experimental design to reduce the influence of this observed variability on experimental outcome. It is possible that biomarkers other than changes in QTc length will be established as superior to QTc length, perhaps some aspect of T-wave behavior that can be assayed more precisely than the length of the QT interval. However, to define in a robust empirical fashion the precise relationship between any putative biomarker and quantification of risk might prove quite difficult owing to the obvious ethical impossibility of potentially requisite studies. We will hopefully find a biomarker or predictor of risk that is technically easier to ‘recognize’ than ‘a change in QTc’. However, knowing the magnitude of that biomarker, in a relatively small group of volunteers, that should lead to nonapproval

Review

of a potential new drug because the risk of sudden cardiac death in humans outweighs the potential benefit of the potential new drug is likely to be more difficult. Acknowledgements

The authors would like to thank Corina Loghin, Lu Zhang and Roy Tamura for their valuable comments. Financial & competing interests disclosure

All authors are full-time employees of Eli Lilly and Company. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Key issues • The error with respect to which the measurement of any individual QT interval adjusted for heart rate (QTc) corresponds to a ‘true’ representation of the time required for ventricular repolarization probably exceeds 25 ms over as few as ten consecutive beats (under the assumption that time for ventricular repolarization is stable over brief periods of time in at-rest healthy individuals). The error in reproducibility of that measurement might exceed 5 ms. Yet, a 5–10 ms mean change is the threshold of interest in thorough QT (TQT) studies. • TQT studies must employ design features and analytical methods that overcome the measurement and variability problems that are impediments to avoiding both false-positive and false-negative results. • A number of alternatives exist with respect to both a variety of design features and a variety of analytical procedures that might serve to reduce the probability of false outcomes. • There has been little, if any, a priori primary comparison of experimental design features or analytical methods to guide the selection of these design and analytical alternatives. • Available data guiding these choices are derived from post hoc analyses of actual TQT studies. • Available data provide reasonable guidance with respect to the number of replicates that should be employed when performing signal averaging of QTc values. • Other design features and analytical methods decisions are, at present, guided largely by intuition. • Both variance and bias are important matters for consideration. • In order to optimize methods for the noninferiority comparison of compound to placebo, a study (multiple studies) could be conducted where placebo is compared with placebo and oversampling (e.g., 15 replicates for signal averaging, 4 days of lead-in prior to the ‘treatment day’) is used, such that multiple methods and analytical procedure combinations can be compared with respect to results. In a placebo versus placebo comparison the mean treatment difference should approach zero (bias should be reduced) and variance around that treatment difference should be minimized.

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•• Describes the most sophisticated thorough QT (TQT) study published to date.

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•• Describes the most sophisticated methodology for correcting the QT interval for the RR interval from standard 10-s ECGs and illustrates its utility in TQT studies.

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Excellent review of a number of the design feature issues in TQT studies.

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Tsong Y, Shen M, Zhong J, Zhang J. Statistical issues of QT prolongation assessment based on linear concentration modeling. J. Biopharm. Stat. 18, 564–584 (2008). Noel GJ, Natarajan J, Chien S, Hunt TL, Goodman DB, Abels R. Effects of three fluoroquinolones on QT interval in healthy adults after single doses. Clin. Pharmacol. Ther. 73, 292–303 (2003). Noel GJ, Goodman DB, Chien S, Solanki B, Padmanabhan M, Natarajan J. Measuring the effects of supratherapeutic doses of levofloxacin on healthy volunteers using four methods of QT correction and periodic and continuous ECG recordings. J. Clin. Pharmacol. 44, 464–473 (2004). Extramiana F, Maison-Blanche P, Cabanis M-J, Ortemann-Renon C, Beaufils P, Leenhardt A. Clinical assessment of drug-induced QT prolongation in association with heart rate changes. Clin. Pharmacol. Ther. 77, 247–258 (2005). Hulhoven R, Rosillon D. Levoceririzine does not prolong the QT/QTc interval in healthy subjects: results from a thorough QT study. Eur. J. Clin. Pharmacol. 63, 1011–1017 (2007).

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•• Describes the current international regulatory standard for thorough QT studies. 102

Biopharmnet www.biopharmnet.com

Affiliations •

Charles M Beasley Jr, MD Lilly Corporate Center, Indianapolis, IN 46285, USA Tel.: +1 317 276 4994 Fax: +1 317 276 0582 [email protected]

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Alex Dmitrienko, PhD Lilly Corporate Center, Indianapolis, IN 46285, USA Tel.: +1 317 276 4994 Fax: +1 317 276 0582



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Malcolm I Mitchell, MD Lilly Corporate Center, Indianapolis, IN 46285, USA Tel.: +1 317 276 4994 Fax: +1 317 276 0582

•• Provides some guidance with respect to selection of baseline and alternative definitions of treatment difference within TQT studies.

www.expert-reviews.com

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Design and analysis considerations for thorough QT studies employing conventional (10 s, 12-lead) ECG recordings.

The QT interval from the ECG cannot be measured precisely. The relationship of the QT interval to the RR interval within individuals across time and d...
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