Cumulative Experience of Azimilide-Associated Torsades de Pointes Ventricular Tachycardia in the 19 Clinical Studies Comprising the Azimilide Database

Pratt, Craig M.; Al‐Khalidi, Hussein R.; Brum, José M.; Holroyde, Michael J.; Schwartz, Peter J.; Marcello, Stephen; Borggrefe, Martin; Dorian, Paul; Camm, A. John

doi:10.1016/j.jacc.2006.04.075

Cited by 44 publications

(28 citation statements)

References 27 publications

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“…TdP can arise months into treatment, 1 and only in a very small subset of the patients receiving a drug 2 (individuals are predisposed to TdP dependent on a large number of factors, such as: the presence of congenital long QT syndromes, heart failure, bradycardia, electrolyte imbalance, gender, hepatic or renal impairment, impaired metabolism, and co-administration of certain drugs 3 ). It is therefore difficult to predict preclinically, or assess during clinical trials on healthy individuals, which drugs will have the potential to cause TdP and which are safe.…”

Section: Introductionmentioning

confidence: 99%

Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk

Mirams

Cui

Sher

et al. 2011

Cardiovascular Research

295

372

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AimsThe level of inhibition of the human Ether-à-go-go-related gene (hERG) channel is one of the earliest preclinical markers used to predict the risk of a compound causing Torsade-de-Pointes (TdP) arrhythmias. While avoiding the use of drugs with maximum therapeutic concentrations within 30-fold of their hERG inhibitory concentration 50% (IC50) values has been suggested, there are drugs that are exceptions to this rule: hERG inhibitors that do not cause TdP, and drugs that can cause TdP but are not strong hERG inhibitors. In this study, we investigate whether a simulated evaluation of multi-channel effects could be used to improve this early prediction of TdP risk.Methods and resultsWe collected multiple ion channel data (hERG, Na, l-type Ca) on 31 drugs associated with varied risks of TdP. To integrate the information on multi-channel block, we have performed simulations with a variety of mathematical models of cardiac cells (for rabbit, dog, and human ventricular myocyte models). Drug action is modelled using IC50 values, and therapeutic drug concentrations to calculate the proportion of blocked channels and the channel conductances are modified accordingly. Various pacing protocols are simulated, and classification analysis is performed to evaluate the predictive power of the models for TdP risk. We find that simulation of action potential duration prolongation, at therapeutic concentrations, provides improved prediction of the TdP risk associated with a compound, above that provided by existing markers.ConclusionThe suggested calculations improve the reliability of early cardiac safety assessments, beyond those based solely on a hERG block effect.

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Section: Introductionmentioning

confidence: 99%

Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk

Mirams

Cui

Sher

et al. 2011

Cardiovascular Research

295

372

View full text Add to dashboard Cite

show abstract

“…The 99th percentile for the QTc interval is 480 milliseconds (ms) and 470 ms in adult women and men, respectively . The risk of TdP increases markedly when the QTc interval is greater than 500 ms . Each 10 ms increase in the QTc interval confers ~6% increase in the risk of a cardiac event .…”

mentioning

confidence: 99%

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation: A Systematic Review

Muensterman

Tisdale

2018

Pharmacotherapy

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Prolongation of the heart rate-corrected QT (QTc) interval increases the risk for torsade de pointes (TdP), a potentially fatal arrhythmia. The likelihood of TdP is higher in patients with risk factors that include female sex, older age, heart failure with reduced ejection fraction, hypokalemia, hypomagnesemia, concomitant administration of two or more QTc interval-prolonging medications, among others. Assessment and quantification of risk factors may facilitate prediction of patients at highest risk for developing QTc interval prolongation and TdP. Investigators have utilized the field of predictive analytics, which generates predictions using techniques including data mining, modeling, machine learning, and others, to develop methods of risk quantification and prediction of QTc interval prolongation. Predictive analytics have also been incorporated into clinical decision support (CDS) tools to alert clinicians regarding patients at increased risk of developing QTc interval prolongation. The objectives of this article are to assess the effectiveness of predictive analytics for identification of patients at risk of drug-induced QTc interval prolongation and to discuss the efficacy of incorporation of predictive analytics into CDS tools in clinical practice. A systematic review of English-language articles (human subjects only) was performed, yielding 57 articles, with an additional 4 articles identified from other sources; a total of 10 articles were included in this review. Risk scores for QTc interval prolongation have been developed in various patient populations including those in cardiac intensive care units (ICUs) and in broader populations of hospitalized or health system patients. One group developed a risk score that includes information regarding genetic polymorphisms; this score significantly predicted TdP. Development of QTc interval prolongation risk prediction models and incorporation of these models into CDS tools reduce the risk of QTc interval prolongation in cardiac ICUs and identify health system patients at increased risk for mortality. The impact of these QTc interval prolongation predictive analytics on overall patient safety outcomes, such as TdP and sudden cardiac death relative to the cost of development and implementation, requires further study.

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“…21 Its clinical antiarrhythmic efficacy appears to be relatively weak. 21 Its clinical antiarrhythmic efficacy appears to be relatively weak.…”

Section: Paulus Kirchhofmentioning

confidence: 99%

New Antiarrhythmic Drugs and New Concepts for Old Drugs

Kirchhof¹

2014

Cardiac Electrophysiology: From Cell to Bedside

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Cumulative Experience of Azimilide-Associated Torsades de Pointes Ventricular Tachycardia in the 19 Clinical Studies Comprising the Azimilide Database

Cited by 44 publications

References 27 publications

Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk

Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation: A Systematic Review

New Antiarrhythmic Drugs and New Concepts for Old Drugs

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