A population pharmacokinetic‐pharmacodynamic analysis and model validation of azimilide

Phillips, Luann; Grasela, Thaddeus H.; Agnew, Jeffrey R.; Ludwig, Elizabeth; Thompson, Gary A.

doi:10.1016/s0009-9236(01)56650-3

Cited by 18 publications

(12 citation statements)

References 6 publications

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“…Population PK-PD modeling of azimilide effects on QTc interval in cardiac patients was reported by Phillips et al 52 The authors did not mention the correction method they used; however, from the other publications on azimilide from the same company, 53 , 54 it may be assumed that it was the Bazett formula (Equation 1). The data of 3 clinical studies were combined for the analysis.…”

Section: Pharmacokinetic-pharmacodynamic Modeling Of Qt Prolongationmentioning

confidence: 99%

Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Piotrovsky¹

2005

AAPS J

109

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A BSTRACTIn this review, factors affecting the QT interval and the methods that are currently in use in the analysis of drug effects on the QT interval duration are overviewed with the emphasis on (population) pharmacokinetic-pharmacodynamic (PK-PD) modeling. Among which the heart rate (HR) and the circadian rhythm are most important since they may interfere with the drug effect and need to be taken into account in the data analysis. The HR effect or the RR interval (the distance between 2 consecutive R peaks) effect is commonly eliminated before any further analysis, and many formulae have been suggested to correct QT intervals for changes in RR intervals. The most often used are Bazett and Fridericia formulae introduced in 1920. They are both based on the power function and differ in the exponent parameter. However, both assume the same exponent for different individuals. More recent fi ndings do not confi rm this assumption, and individualized correction is necessary to avoid under-or overcorrection that may lead to artifi cial observations of drug-induced QT interval prolongation. Despite the fact that circadian rhythm in QT and QTc intervals is a welldocumented phenomenon, it is usually overlooked when drug effects are evaluated. This may result in a false-positive outcome of the analysis as the QTc peak due to the circadian rhythm may coincide with the peak of the drug plasma concentration. In view of these effects interfering with a potential drug effect on the QTc interval and having in mind low precision of QT interval measurements, a preferable way to evaluate the drug effect is to apply a population PK-PD modeling. In the literature, however, there are only a few publications in which population PK-PD modeling is applied to QT interval prolongation data, and they all refer to antiarrhythmic agents. In this review, after the most important sources of variability are outlined, a comprehensive population PK-PD model is presented that incorporates an individualized QT interval correction, a circadian rhythm in the individually corrected QT intervals, and a drug effect. The model application is illustrated using real data obtained with 2 compounds differing in their QT interval prolongation potential. The usefulness of combining data of several studies is stressed. Finally, the standard approach based on the raw observations and formal statistics, as described in the Preliminary Concept paper of the International Conference on Harmonization, is briefl y compared with the method based on population PK-PD modeling, and the advantages of the latter are outlined.

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Section: Pharmacokinetic-pharmacodynamic Modeling Of Qt Prolongationmentioning

confidence: 99%

Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Piotrovsky¹

2005

AAPS J

109

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“…Modeling of the exposure-response (ER) relationship is certainly not a new tool and has been applied to QT data before the implementation of the TQT study, especially for antiarrhythmic drugs (Allen et al 2000;Holford et al 1981;Phillips et al 2001;Piergies et al 1987;Shi et al 2001;Whiting et al 1980). With the application of ER to data derived from TQT studies, the role of the methodology for non-antiarrhythmic drugs has expanded to include projection of QTc prolongation with doses and formulations not directly evaluated in the TQT study and projections of QTc prolongation in specific patient populations with increased exposure to a drug and, occasionally, to help to understand ambiguous results.…”

Section: The Role Of Exposure-response Relationshipmentioning

confidence: 99%

Clinical ECG Assessment

Darpö

2015

Principles of Safety Pharmacology

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With the adoption of the ICH E14 guidance, the thorough QT/QTc (TQT) study has become the focus of clinical assessment of an NCE's effects on ECG parameters. The TQT study is used as a guide to the liability of a drug to cause proarrhythmias on the basis of delayed cardiac repolarization. Around 300 TQT studies have been performed since 2005 and through interactions between sponsors and regulators, especially FDA's Interdisciplinary Review Team (IRT) for QT studies. These studies can today be performed more effectively and with great confidence in the generated data. This chapter will discuss technical features and the design and analysis of TQT studies, how assay sensitivity is demonstrated, and examples from recently conducted studies. ECG assessment for drugs that cannot be safely given to healthy volunteers is also addressed, and examples from studies in cancer patients and in healthy volunteers with tyrosine kinase inhibitors are discussed. The TQT study is resource intensive and designed to solely evaluate whether an NCE prolongs the QTc interval. If data with similar confidence can be generated from other studies that are routinely performed as part of the clinical development, this would represent a more optimal use of human resources. Methods and approaches to increase the confidence in ECG data derived from "early QT assessment" in single-ascending/multiple-ascending dose studies are therefore discussed, and a path toward replacing the TQT study using these approaches will be outlined.

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“…35 For orally administered azimilide, an E max model described the concentration-effect relationship with mean E max and EC 50 estimates of 62 ms and 460 ng/mL, respectively. 33 With use of E max model and data obtained after intravenous infusion of 0.5 mg dofetilide to healthy volunteers, mean E max was 121 ms and mean EC 50 was 2.2 ng/mL. 34 E maxtype models have rarely been reported for non-antiarrhythmic drugs.…”

Section: Pharmacokinetic / Pharmacodynamic Modeling In Qt Investigationsmentioning

confidence: 99%

Practice and challenges of thorough QT studies

Stockbridge¹,

Zhang²,

Garnett³

et al. 2012

Journal of Electrocardiology

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A population pharmacokinetic‐pharmacodynamic analysis and model validation of azimilide

Cited by 18 publications

References 6 publications

Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Clinical ECG Assessment

Practice and challenges of thorough QT studies

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