Risks encountered when not adjusting for diurnal variation and food effect in QTcF analysis based on phase I data

Sep 30, 2026

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Phase I single and multiple ascending dose studies increasingly support QT liability evaluation for new drugs, yet they are often not designed for concentration-QT analysis or for documenting key influences like meal intake. Sampling times can also vary during the day. This simulation assesses the reliability of Garnett et al.’s standard pre-specified PLM versus an adjusted PLM incorporating food effect and clock time using 1000 simulations of QTcF profiles with mild QT liability, comparing unadjusted and adjusted negative rates under multiple scenario designs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

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Introduction

Methods

Simulation scenarios and settings

Journal of Pharmacokinetics and Pharmacodynamics (2026) 53:6 [https://doi.org/10.1007/s10928-025-10012-9](https://doi.org/10.1007/s10928-025-10012-9) ORIGINAL PAPER Risks encountered when not adjusting for diurnal variation and food effect in QTcF analysis based on phase I data Maddlie Bardol1 · Andrea Henrich1 · Celine Sarr1 · Enrica Mezzalana1 · Jurgen Langenhorst1 Received: 25 November 2024 / Accepted: 15 November 2025 / Published online: 5 January 2026 © The Author(s) 2026 Abstract Phase I single and multiple ascending dose studies are more and more often used to evaluate QT liability of new drugs. However, these studies are not primarily tailored to concentration-QT analysis and to control or document influential factors such as meal intake. In addition, sampling times may vary over the day for operational reasons. This simulation analysis evaluates the reliability of the standard pre-specified linear model (PLM) proposed by a publication of Garnett et al. and an adjusted PLM accounting for food effect and clock time. The QTcF-time profile of a drug with a mild QTliability (upper bound of the 90% confidence interval close to the 10 ms threshold) resulting from a well-controlled study was simulated 1000 times and evaluated with the unadjusted PLM (Scenario A, negative rate: 20.8%) . Compared to suboptimal study designs with uncontrolled and unbalanced (i.e., differences between active treatment and placebo) differences in meal intake and dosing/sampling times, the unadjusted PLM led to an inflated negative rate (≤ 50%), while the adjusted PLM was able to correct for the imbalances resulting in similar negative rates as the reference scenario or lower, i.e., being more conservative. In conclusion, good documentation in Phase I trials and adjusting for known influential factors can help to analyze QT effects reliably and waive with relevance QT/QTc studies. Keywords QTc modeling · Pharmacometrics · QT interval Introduction In early Phase I studies, doses of a new drug are usually evaluated using randomized, placebo-controlled designs enrolling a small number of healthy volunteers. One aim of these studies is to characterize the drug’s initial safety and tolerability profile. A key safety aspect is the potential of a drug to delay cardiac repolarization and increase the risk ofarrythmia. The degree of QT prolongation on the surface electrocardiogram (ECG), assessed using the Fridericia corrected QT interval (QTcF), can be used as a biomarker to evaluate the proarrhythmic risk [1] . The modelling approach proposed by Garnett et al. [2] can be applied to the data collected in Phase I studies to assess if the drug effect on the QT interval is of concern. In case of demonstration of a safe profile, this analysis can be used to prevent extensive QT assessment in target 􀀍 Maddlie Bardol [Maddlie.bardol@pharmetheus.com](Maddlie.bardol@pharmetheus.com) 1 PharmetheusAB, Kungsängstull 4, Uppsala 753 19, Sweden patient populations during later stages of drug development. However, a large variability in QT intervals is commonly observed in Phase I studies due to several external factors including diurnal variations and the time of food intake not always controlled [3–6] . Furthermore, the sampling design may not be primarily tailored to the concentration-QT analyses. In the following context, the pre-specified linear mixedeffects model (PLM) proposed by Garnett et al. needs tobe adjusted to ensure appropriateness of the analysis and account for key factors such as time of meals, dosing and sampling. To account for the diurnal variation on the QTcF interval, Minocha et al. [7] published a model describing the QTcF profile over clock time in healthy subjects receiving placebo. The effect of food intake on QT interval has also been described by others [6] . From these different papers [6, 7], it is apparent that a lack of sufficient control on key parameters as clock-time and food status, may introduce bias in

FAQs

Why are Phase I studies challenging for concentration-QTc analysis?

They are often not primarily tailored to concentration-QT analysis and may not control or document influential factors such as meal intake, while sampling times can vary across the day.

What models are compared in the simulation analysis?

The study evaluates the reliability of the standard pre-specified linear model (PLM) proposed by Garnett et al. and an adjusted PLM that accounts for food effect and clock time.

How does adjusting for food effect and clock time affect the results?

The unadjusted PLM produced an inflated negative rate under suboptimal, uncontrolled scenarios, while the adjusted PLM corrected imbalances and yielded negative rates comparable to or lower than the reference scenario.

Sep 30, 2026

Upload by Rowan