Evidence map›Paper›PMID 41493500›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2026

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

Maddlie Bardol, Andrea Henrich, Celine Sarr, Enrica Mezzalana, Jurgen Langenhorst

Abstract read
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maddlie BardolPharmetheus AB, Kungsängstull 4, Uppsala, 753 19, Sweden. Maddlie.bardol@pharmetheus.com.
Andrea HenrichPharmetheus AB, Kungsängstull 4, Uppsala, 753 19, Sweden.
Celine SarrPharmetheus AB, Kungsängstull 4, Uppsala, 753 19, Sweden.
Enrica MezzalanaPharmetheus AB, Kungsängstull 4, Uppsala, 753 19, Sweden.
Jurgen LangenhorstPharmetheus AB, Kungsängstull 4, Uppsala, 753 19, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 QT-liability (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.

Indexed as

Circadian RhythmClinical Trials, Phase I as TopicFood-Drug InteractionsLong QT SyndromeComputer SimulationDose-Response Relationship, DrugElectrocardiographyHumansLinear ModelsPharmacometricsQTc modelingQT interval

Identifiers

PMID41493500
PMCPMC12775080

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.