QT/QTc Prolongation in Placebo-Treated Subjects: a PhRMA Collaborative Data Analysis

A Systematic Review and Pooled Analysis of Select Safety Parameters Among Normal Healthy Volunteers Taking Placebo in Phase 1 Clinical Trials

A systematic review of the Bristol‐Myers Squibb normal healthy volunteers (NHVs) database identified phase 1 trials that included NHVs administered placebo with the aim of characterizing normal inter‐ and intraindividual safety parameter variability. Twenty‐five single and multiple ascending dose studies, median duration 28 (2 to 63) days, were included in the pooled analysis (355 NHVs). Laboratory evaluations, vital signs, electrocardiograms, and adverse events were assessed. The most commonly occurring adverse event was headache (28 [7.9%] NHVs; 519.5 events/100 person‐years). During the dosing period (on placebo), evaluations showed 5.1 events/100 measures of alanine aminotransferase and 7.3 events/100 measures of creatine kinase 1× above the upper limit of normal. Alanine aminotransferase and creatine kinase elevations occurred in 28 (7.9%) and 39 (11.0%) NHVs, respectively; 105 (30.3%) NHVs had low and 46 (13.3%) had high diastolic blood pressure. This analysis may inform future study designs and provide a context for interpretation of safety signals in early phase clinical trials.

Timing of pre-dose baseline electrocardiograms in clinical trials: increased sampling over a prolonged baseline period worsens variance of QTc

BACKGROUND AND PURPOSE

The US Food and Drug Administration (US FDA) currently recommends recording of electrocardiograms (ECGs) prior to drug administration in thorough QT studies over an hour or more time to improve reliability of baseline ECG values. However, the baseline period is usually in the morning during a period of intense trial activity and rapid circadian change in QTc. The purpose of this study was to determine if the practice of recording an extended baseline does, in fact, decrease QTc variance at baseline.

METHODS

ECG data from three thorough QT studies (TQTS) in which three ECGs (commonly referred to as triplicates) were recorded at each of three pre-specified time points during the 60 to 90 minutes before drug administration were analyzed by determining the intra-subject and inter-subject standard deviation (SD) of QTcF (Fridericia-correct QT) for each of the three pre-drug time points and for the three time points combined.

RESULTS

QTcF was relatively normally distributed in each study. Intra-subject variability of QTcF was greater for the combined triplicate recordings than for the individual triplicates at baseline treatment time points in 39 of 42 cases (93%). This was the case in 48% of the comparisons in the inter-subject analysis.

CONCLUSIONS

The practice of recording three sets of triplicate ECGs over an hour or more before drug administration in a TQTS increases variability of baseline QTcF consistently in cross-over designed trials, and in roughly half of parallel comparisons. Higher variability suggests that the three-triplicate approach does not provide a more reliable baseline value. Less variability of QTcF can be obtained by simply recording one triplicate prior to drug administration. This principal may apply to other ECG and other physiological variables that have a monotonic circadian trend or that may be affected by intense trial activity during the pre-drug hour.

Statistical models for heart rate correction of the QT interval

The analysis of QT interval data is now an essential part of the assessment of drug safety. As the QT interval is inversely associated with heart rate, an appropriate correction must be applied in order to evaluate QT data in clinical trials. The aim is to characterize changes in QT interval at a standard heart rate, taking into account the correlation between these two variables to adjust for heart rate changes during the course of the trial. It has been shown that the relationship between the RR interval (=1/heart rate) and the QT interval is highly variable between individuals but stable over time within each individual.Many mathematical models have been developed to describe the QT-RR relationship. However, there has been less emphasis on the derivation of suitable statistical models that account for the multilevel structure of the ECG data.An important example is the interpretation of the so-called population-specific heart rate corrections, which are based on data pooled from different subjects. Often, simple regression techniques are used to quantify the population correction, disregarding the subject level and leading to biased parameter estimates. Instead, population-based corrections that account for individual intercepts should be used, in order to distinguish within-subject-effects from between-subject effects. Therefore, population-specific corrections cannot be derived solely from the cross-sectional data. The impact of the different statistical models is illustrated by data from the baseline periods of six clinical QT studies.