Christine Garnett

Impact of Pharmacometric Reviews on New Drug Approval and Labeling Decisions—a Survey of 31 New Drug Applications Submitted Between 2005 and 2006

Exploratory analyses of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression are often referred to as the pharmacometrics (PM) analyses. The objective of the current report is to assess the role of PM, at the Food and Drug Administration (FDA), in drug approval and labeling decisions. We surveyed the impact of PM analyses on New Drug Applications (NDAs) reviewed over 15 months in 2005–2006. The survey focused on both the approval and labeling decisions through four perspectives: clinical pharmacology primary reviewer, their team leader, the clinical team member, and the PM reviewer. A total of 31 NDAs included a PM review component. Review of NDAs involved independent quantitative evaluation by FDA pharmacometricians. PM analyses were ranked as important in regulatory decision making in over 85% of the 31 NDAs. Case studies are presented to demonstrate the applications of PM analysis.

Results From the IQ‐CSRC Prospective Study Support Replacement of the Thorough QT Study by QT Assessment in the Early Clinical Phase

The QT effects of five “QT‐positive” and one negative drug were tested to evaluate whether exposure–response analysis can detect QT effects in a small study with healthy subjects. Each drug was given to nine subjects (six for placebo) in two dose levels; positive drugs were chosen to cause 10 to 12 ms and 15 to 20 ms QTcF prolongation. The slope of the concentration/ΔQTc effect was significantly positive for ondansetron, quinine, dolasetron, moxifloxacin, and dofetilide. For the lower dose, an effect above 10 ms could not be excluded, i.e., the upper bound of the confidence interval for the predicted mean ΔΔQTcF effect was above 10 ms. For the negative drug, levocetirizine, a ΔΔQTcF effect above 10 ms was excluded at 6‐fold the therapeutic dose. The study provides evidence that robust QT assessment in early‐phase clinical studies can replace the thorough QT study.

Dealing with Global Safety Issues

Drug-induced torsade de pointes (TdP) is a potentially fatal iatrogenic entity. Its reporting rate in association with non-cardiac drugs increased exponentially from the early 1990s and was associated with an increasing number of new non-cardiac drugs whose proarrhythmic liability was not appreciated pre-marketing. This epidemic provoked a comprehensive global response from drug regulators, drug developers and academia, which resulted in stabilization of the reporting rate of TdP. This commentary reviews the chronology and nature of, and the reasons for, this response, examines its adequacy, and proposes future strategies for dealing with such iatrogenic epidemics more effectively. It is concluded that the response was piecemeal and lacked direction. No one entity was responsible, with the result that important contributions from regulators, industry and academia lacked coordination. While the process of dealing with QT crisis seemed to have worked reasonably well in this instance, it does not seem wise to expect the next crisis in drug development to be managed as well. Future crises will need better management and the challenge is to implement a system set up to respond globally and efficiently to a perceived drug-related hazard. In this regard, we discuss the roles of new tools the legislation has provided to the regulators and the value of an integrated expert assessment of all pre-approval data that may signal a potential safety issue in the postmarketing period. We also discuss the roles of other bodies such as the WHO Collaborating Centre for International Drug Monitoring, CIOMS and the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).

Methodologies to characterize the QT/corrected QT interval in the presence of drug-induced heart rate changes or other autonomic effects

This White Paper, written collaboratively by members of the Cardiac Safety Research Consortium from academia, industry, and regulatory agencies, discusses different methods to characterize the QT effects for drugs that have a substantial direct or indirect effect on heart rate. Descriptions and applications are provided for individualized QT-R-R correction, Holter bin, dynamic QT beat-to-beat, pharmacokinetic-pharmacodynamic modeling, and QT assessment at constant heart rate. Most of these techniques are optimally performed using continuous electrocardiogram data obtained in clinical studies designed to characterize a drugs effect on the QT interval. An important study design element is the collection of drug-free data over a range of heart rates seen on treatment. The range of heart rates is increased at baseline by using ambulatory electrocardiogram recordings in addition to those collected under semisupine, resting conditions. Discussions in this study summarize areas of emerging consensus and other areas in which consensus remains elusive and provide suggestions for additional research to further increase our knowledge and understanding of this topic.

Creation of a Knowledge Management System for QT Analyses

An increasing number of thorough QT (TQT) reports are being submitted to the US Food and Drug Administrations interdisciplinary review team for QT (IRT‐QT), requiring time‐intensive quantitative analyses by a multidisciplinary review team within 45 days. This calls for systematic learning to guide future trials and policies by standardizing and automating the QT analyses to improve review efficiency, provide consistent advice, and enable pooled data analyses to answer key regulatory questions. The QT interval represents the time from initiation of ventricular depolarization to completion of ventricular repolarization recorded by electrocardiograph (ECG) and is used in the proarrhythmic risk assessment. The developed QT knowledge management system is implemented in the R package “QT.” Data from 11 crossover TQT studies including time‐matched ECGs and pharmacokinetic measurements following single doses of 400 to 1200 mg moxifloxacin were used for the QT analysis example. The automated workflow was divided into 3 components (data management, analysis, and archival). The generated data sets, scripts, tables, and graphs are automatically stored in a queryable repository and summarized in an analysis report. More than 100 TQT studies have been analyzed using the system since 2007. This has dramatically reduced the time needed to review TQT studies and has made the IRT‐QT reviews consistent across reviewers. Furthermore, the system enables leveraging prior knowledge through pooled data analyses to answer policy‐related questions and to understand the various effects that influence study results.

Improving the Assessment of Heart Toxicity for All New Drugs Through Translational Regulatory Science

Fourteen drugs have been removed from the market worldwide because they cause torsade de pointes. Most drugs that cause torsade can be identified by assessing whether they block the human ether à gogo related gene (hERG) potassium channel and prolong the QT interval on the electrocardiogram. In response, regulatory agencies require new drugs to undergo “thorough QT” studies. However, some drugs block hERG potassium channels and prolong QT with minimal torsade risk because they also block calcium and/or sodium channels. Through analysis of clinical and preclinical data from 34 studies submitted to the US Food and Drug Administration and by computer simulations, we demonstrate that by dividing the QT interval into its components of depolarization (QRS), early repolarization (J−Tpeak), and late repolarization (Tpeak−Tend), along with atrioventricular conduction delay (PR), it may be possible to determine which hERG potassium channel blockers also have calcium and/or sodium channel blocking activity. This translational regulatory science approach may enable innovative drugs that otherwise would have been labeled unsafe to come to market.

A Modeling and Simulation Approach to Characterize Methadone QT Prolongation Using Pooled Data From Five Clinical Trials in MMT Patients

Pharmacokinetic (PK)‐pharmacodynamic modeling and simulation were used to establish a link between methadone dose, concentrations, and Fridericia rate‐corrected QT (QTcF) interval prolongation, and to identify a dose that was associated with increased risk of developing torsade de pointes. A linear relationship between concentration and QTcF described the data from five clinical trials in patients on methadone maintenance treatment (MMT). A previously published population PK model adequately described the concentration–time data, and this model was used for simulation. QTcF was increased by a mean (90% confidence interval (CI)) of 17 (12, 22) ms per 1,000 ng/ml of methadone. Based on this model, doses >120 mg/day would increase the QTcF interval by >20 ms. The model predicts that 1–3% of patients would have ΔQTcF >60 ms, and 0.3–2.0% of patients would have QTcF >500 ms at doses of 160–200 mg/day. Our predictions are consistent with available observational data and support the need for electrocardiogram (ECG) monitoring and arrhythmia risk factor assessment in patients receiving methadone doses >120 mg/day.

Highly automated QT measurement techniques in 7 thorough QT studies implemented under ICH E14 guidelines.

Thorough QT (TQT) studies are designed to evaluate potential effect of a novel drug on the ventricular repolarization process of the heart using QTc prolongation as a surrogate marker for torsades de pointes. The current process to measure the QT intervals from the thousands of electrocardiograms is lengthy and expensive. In this study, we propose a validation of a highly automatic‐QT interval measurement (HA‐QT) method. We applied a HA‐QT method to the data from 7 TQT studies. We investigated both the placebo and baseline‐adjusted QTc interval prolongation induced by moxifloxacin (positive control drug) at the time of expected peak concentration. The comparative analysis evaluated the time course of moxifloxacin‐induced QTc prolongation in one study as well. The absolute HA‐QT data were longer than the FDA‐approved QTc data. This trend was not different between ECGs from the moxifloxacin and placebo arms: 9.6 ± 24 ms on drug and 9.8 ± 25 ms on placebo. The difference between methods vanished when comparing the placebo‐baseline‐adjusted QTc prolongation (1.4 ± 2.8 ms, P = 0.4). The differences in precision between the HA‐QT and the FDA‐approved measurements were not statistically different from zero: 0.1 ± 0.1 ms (P = 0.7). Also, the time course of the moxifloxacin‐induced QTc prolongation adjusted for placebo was not statistically different between measurements methods.

Clinical pharmacology as a cornerstone of orphan drug development.

A recent US Food and Drug Administration (FDA) advisory committee meeting highlighted the potential of clinical pharmacology to overcome challenges in orphan drug development.

Electrocardiographic data quality in thorough QT/QTc studies.

BackgroundMost drugs with systemic bioavailability have to undergo a thorough QT (TQT) study, which includes a pharmacologic positive control. A set of QTc-quality tests was recently proposed with the possible aim of removing the need for a positive control.ObjectiveWe evaluated the influence of QT measurement and QTc computation methodology on the proposed QTc-quality tests.MethodsThe baseline ECG waveforms and fiducial points were retrieved from 34 crossover TQT studies that had full-day baseline ECGs prior to each study period. The QT measurement methodology and recorder type were retrieved from study reports. The influence of QTc computation methodology was investigated by applying the pattern-matching technique and/or using the 10-s average heart rate.ResultsThere were no statistical differences in any QTc-quality test values between studies using continuous or 10-s bedside recordings and, in a subset of the quality test, an increase of data quality for semi-automatically read studies compared with those manually read (pxa0<xa00.01). There was a significant improvement (pxa0<xa00.01) in all the QTc-quality test values for QTc measurements obtained by using pattern matching with or without 10-s average heart rate in comparison to the original QTc measurements.ConclusionThe findings suggest that QTc quality is mostly driven by the QTc measurement methodology rather than other study-related factors.