The ICH E14 guidance recommends that all new drugs with systemic bioavailability are assessed for the ability to delay cardiac repolarization as measured by the QT/QTc interval on the surface ECG. For most drugs, this evaluation is performed in the Thorough QT/QTc (TQT) study. Could using model-based approaches during routine early studies influence the current paradigm for clinical QT assessment?
A change of heart on quantifying QT effects
In first-in-man single-ascending dose (SAD) studies, small cohorts of healthy volunteers receive escalating doses of the new chemical entity (NCE) often up to the maximally tolerated dose. Sponsors often use high quality ECG monitoring in these studies to gain an early understanding of a drug’s potential cardiotoxicity. Could “early QT assessment” serve as an alternative path to quantify QT effects to increase the efficiency of drug development?
As a member of the Cardiac Safety Research Consortium (CSRC), I’ve had the privilege of helping to answer this question. On December 12th, I participated in the CSRC Workshop where we presented the results from a collaboration between CSRC and the Consortium for Innovation and Quality in Pharmaceutical Development (IQ)— the Prospective IQ-CSRC Clinical Study.
The study evaluated five drugs with well-established QTc effects and one drug known not to prolong the QT interval. The doses were selected by the FDA to obtain QTc prolongation of 10-12ms, which is around the regulatory threshold. Twenty healthy subjects participated in a randomized, placebo-controlled, incomplete block design study; nine subjects received two doses (low and high) of each drug on consecutive days. Six subjects received placebo.
Because a small number of subjects in a SAD study receive each drug dose level, the standard “by time point” analysis would be under powered and thus, inappropriate. We used linear mixed-effects ER models to analyze the data pooled across all dose levels which increases the precision of the estimated QTc effect. The following criteria were used to determine if a drug significantly prolonged the QT interval:
- QT-positive drugs will have an upper bound (UB) of the 2-sided 90% confidence interval (CI) of the projected QTc effect at the peak plasma level of the lower dose greater than the threshold for regulatory concern (10ms) and a positive slope for the ER relationship.
- QT-negative drugs will have an UB of the CI of the projected QTc effect of the higher dose that is less than 10ms.
Results and conclusions from the study
We published the results of this study in Clinical Pharmacology and Therapeutics. As expected, the five QT-positive drugs had a significantly positive slope for the concentration/ΔQTc relationship. For the lower dose of these drugs, the upper bound of the CI for the predicted mean ΔΔQTcF effect was above 10ms. A supratherapeutic dose of the negative drug did not result in a ΔΔQTcF effect above 10ms. These data provide evidence that intense ECG assessment in a SAD study paired with ER analysis can detect QTc prolongation and provides a pathway to waive the TQT study.
Assay sensitivity validates that the study could detect QT prolongation by a drug. While TQT studies use moxifloxicin as a positive control for assay sensitivity, phase 1 studies will not use it. Further research is needed to explore whether intrinsic variability in ECG measurements can be used to quantify assay sensitivity.
Supporting efficient, effective and economical drug development strategies
At Certara, I lead a team that supports our clients by building quantitative models that explain the relationship between dosing regimen and cardiac safety profile. We can also perform PK/PD modeling of other biomarkers for cardiac safety such as blood pressure or non-QT interval data (RR, PR, and QRS). Our goal is to help clients improve their interactions with regulatory agencies to get important drugs approved faster.
Want to be able to quickly and easily analyze ECG data?
Certara Strategic Consulting can help our clients assess the potential cardiac liability of their drug candidates. I hope that you’ll read this case study on how we used concentration-QTc modeling to assess the safety profile of a treatment for metastatic breast cancer. Let me know what you think in the comments section below!