Assessing the Probability of Drug-Induced QTc-Interval Prolongation During Clinical Drug Development

Chain, Anne; Krudys, KM; Danhof, Meindert; Pasqua, Oscar Della

doi:10.1038/clpt.2011.202

Cited by 25 publications

(36 citation statements)

References 35 publications

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“…Nevertheless, there is still no clear consensus on when Bayesian methods should be chosen over frequentist approaches [37]. Unlike the probability curve reported in [24,30], where only the posterior distribution of the typical value of the drug effect parameter was considered (which provides the probability curve of QT-interval prolongation greater than a certain threshold for the typical subject), herein a more thorough approach was introduced where the posterior distributions of both the typical parameter and the interindividual variability were accounted for. Therefore, both the uncertainty of the parameter estimates and the interindividual variability are used to calculate the probability curve.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, due to the known prolonging effect on the QT interval, moxifloxacin has already been the subject of translational investigations to assess the potential relationship between dogs and humans [24,28,29]. In view of this, the development of a pharmacokinetic/pharmacodynamic (PK/PD) model to investigate the drug-induced QT-interval prolongation in dogs and humans as well as the assessment of the probability of QT-interval prolongation greater than a specified threshold could be suitable to evaluate the propensity of compounds to prolong the QT interval, as already reported in [30].…”

Section: Introductionmentioning

confidence: 99%

“…The probability of QT-interval prolongation greater than a critical threshold is straightforwardly calculated when the PK/PD model is implemented according to a fully Bayesian approach [30]. Herein we introduce a model building procedure through which, first, the best model is chosen according to the widely accepted Non-linear Mixed Effect frequentist approach and subsequently the final model is implemented according to the fully Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling of drug-induced QT-interval prolongation: estimation approaches and translational opportunities

Marostica

Ammel²,

Teisman³

et al. 2015

J Pharmacokinet Pharmacodyn

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Safety pharmacology studies are performed to assess whether compounds may provoke severe arrhythmias (e.g. Torsades de Pointes, TdP) and sudden death in man. Although there is strong evidence that drugs inducing TdP in man prolong the QT interval in vivo and block the human ether-a-go-go-related gene (hERG) ion channel in vitro, not all drugs affecting the QT interval or the hERG will induce TdP. Nevertheless, QT-interval prolongation and hERG blockade currently represent the most accepted early risk biomarkers to deselect drugs. An extensive pharmacokinetic/pharmacodynamic (PK/PD) analysis is developed to understand moxifloxacin's-induced effects on the QT interval by comparing the relationship between results of an in vitro patch-clamp model to in vivo models. The frequentist and the fully Bayesian estimation procedures were compared and provided similar performances when the best model selected in NONMEM is subsequently implemented in WinBUGS, which guarantees a straightforward calculation of the probability of QT-interval prolongation greater than 2.5 % (10 ms). The use of the percent threshold to account for the intrinsic differences between species and a new calculation of the probability curve are introduced. The concentration providing the 50 % probability indicates that dogs are more sensitive than humans to QT-interval prolongation. However, based on the drug effect, a clear distinction between species cannot be made. An operational PK/PD model of agonism was used to investigate the relationship between effects on the hERG and QT-interval prolongation in dogs. The proposed analysis contributes to establish a translational relationship that could potentially reduce the need for thorough QT studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling of drug-induced QT-interval prolongation: estimation approaches and translational opportunities

Marostica

Ammel²,

Teisman³

et al. 2015

J Pharmacokinet Pharmacodyn

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show abstract

“…An important determinant for development of TdP is the prolongation of the QT interval, mostly arising from the prolongation of the action potential (AP) which affects the rapid potassium current (I kr ) by inhibition of Ether-a-go-go-Releted Gene (hERG) channel in CMs [1,4]. Currently, assessing the risk of drug-induced QT interval prolongation is one of the main aspects of the standard preclinical drug evaluation [5,6]. The assessments should be preferably based on repeated-dose toxicity tests.…”

Section: Introductionmentioning

confidence: 99%

In vitroModel for Assessing Arrhythmogenic Properties of Drugs Based on High-resolution Impedance Measurements

Nguemo

Šarić

Pfannkuche

et al. 2012

Cell Physiol Biochem

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Background/Aims: Cardiac dysfunction is one of the main cause of drug candidate failures in the preclinical and/or clinical studies and responsible for the retraction of large number of drugs from the market. The prediction of arrhythmic risk based on preclinical trials during drug development remains limited despite intensive and costly investigation. Moreover, methods for analyzing beating behavior of cardiomyocytes (CMs) in culture to diagnose arrhythmias are not well developed. Methods: In this study, we combined two emerging technologies, induced pluripotent stem (iPS) cell-derived CMs and impedance-based real-time (xCELLigence RTCA Cardio Instrument) monitoring of CM electrical activity, to assess the effect of drugs known affect cardiac activity such as isoproterenol, carbachol, terfenadine, sotalol and doxorubicin. Cells were exposed to a drug in a single dose or repeated dose scenarios and data were analyzed using RTCA Cardio software, Poincaré plot and detrended fluctuation analysis. Results: The results revealed significant changes in beating parameters of iPS-CMs induced by reference compounds. Heptanol, gap junction blocker, completely disrupted the synchronous beating pattern of iPS-CMs. Decrease of beating rate, amplitude and beat-to-beat signal variations of iPS-CMs monolayer observed in the presence of doxorubicin revealed severe abnormality detected by the system. Additionally, the irregular beating rhythms recorded in the presence of Terfenadine and Sotalol at high concentration, reflect abnormalities in cell contraction and/or relaxation which may lead to arrhythmia. Conclusions: All these results indicated that xCELLigence RTCA Cardio system combined with iPS cells, has the potential to be an attractive high-throughput tool for studying CMs during prolonged culture times and to screen potential drugs for cardiotoxic side effects.

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“…Methods for PKPD model development are detailed in Bergenholm et al (2016). Baseline variability of JT intervals was minimized applying a circadian rhythm and RR correction models (Chain et al, 2011; Bergenholm et al, 2016). The PK and PD were modeled sequentially, and Model 10 (Table 1) was selected to describe the drug effect.…”

Section: Resultsmentioning

confidence: 99%

Parameter Identifiability of Fundamental Pharmacodynamic Models

et al. 2016

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Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably “good” standard errors may sometimes mask unidentifiability issues.

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Assessing the Probability of Drug-Induced QTc-Interval Prolongation During Clinical Drug Development

Cited by 25 publications

References 35 publications

Modelling of drug-induced QT-interval prolongation: estimation approaches and translational opportunities

Modelling of drug-induced QT-interval prolongation: estimation approaches and translational opportunities

In vitroModel for Assessing Arrhythmogenic Properties of Drugs Based on High-resolution Impedance Measurements

Parameter Identifiability of Fundamental Pharmacodynamic Models

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