In vitro–in vivo extrapolation of drug-induced proarrhythmia predictions at the population level

Polak, Sebastian; Wiśniowska, Barbara; Fijorek, Kamil; Glinka, Anna; Mendyk, Aleksander

doi:10.1016/j.drudis.2013.10.009

Cited by 23 publications

(18 citation statements)

References 61 publications

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“…This complex interplay of the parent drug, its metabolites, and the inhibitory effect on multiple cardiac ion channels may all contribute to the continued debate on the cardiac safety associated with CT (3,(10)(11)(12). Until recently, torsadogenicity assessments were typically focused on the inhibition of the cardiac I Kr current occurring at the level of the hERG channel (encoded by the human ether-a-go-go gene) (13)(14)(15)(16). Over the last few years, the comprehensive in vitro proarrythmia assay (CiPA) initiative has been advocating the in vitro assessment of multiple cardiac ion currents and the estimation of the combined effect of multiple ionic channel inhibition using cardiomyocyte cell-based quantitative systems models (17,18).…”

Section: Introductionmentioning

confidence: 99%

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

Patel

Wiśniowska

Jamei

et al. 2017

AAPS J

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Abstract.A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I Kr , I Ks , I CaL ); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.

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Section: Introductionmentioning

confidence: 99%

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

Patel

Wiśniowska

Jamei

et al. 2017

AAPS J

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“…Physiological parameters influence the clinically observed drug-triggered ECG disruption and reactivity on drugs [25]. Therefore, the development of physiological parameters models, estimated with the use of rich, multicenter clinical data, can help to properly mimic populations involved in the clinical trials.…”

Section: Discussionmentioning

confidence: 99%

“…The LVPWd was used as a surrogate of the left heart wall thickness and it was hypothesized that by modifying the length of the string of virtual cells according to the developed LVPWd models one can influence the electrophysiological model outputs and therefore more reliably predict the clinically expected inter-individual variability [25]. Two BDMM’s outputs were simulated and both of them were derived from the simulated pseudo-ECG traces, i.e., QT and QT corrected by the heart rate with the Fridericia equation (QTcF).…”

Section: Methodsmentioning

confidence: 99%

Model of the Distribution of Diastolic Left Ventricular Posterior Wall Thickness in Healthy Adults and Its Impact on the Behavior of a String of Virtual Cardiomyocytes

Fijorek

Tanner

Stähli

et al. 2014

J. of Cardiovasc. Trans. Res.

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Correlation of the thickness of the left ventricular posterior wall (LVPWd) with various parameters, including age, gender, weight and height, was investigated in this study using regression models. Multicenter derived database comprised over 4,000 healthy individuals. The developed models were further utilized in the in vitro–in vivo (IVIV) translation of the drug cardiac safety data with use of the mathematical model of human cardiomyocytes operating at the virtual healthy population level. LVPWd was assumed to be equivalent to the length of one-dimensional string of virtual cardiomyocyte cells which was presented, as other physiological factors, to be a parameter influencing the simulated pseudo-ECG (pseudoelectrocardiogram), QTcF and ∆QTcF, both native and modified by exemplar drug (disopyramide) after IKr current disruption. Simulation results support positive correlation between the LVPWd and QTcF/∆QTc. Developed models allow more detailed description of the virtual population and thus inter-individual variability influence on the drug cardiac safety.Electronic supplementary materialThe online version of this article (doi:10.1007/s12265-014-9558-4) contains supplementary material, which is available to authorized users.

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“…Computational heart models have been demonstrated to be useful and also predictive, providing a strong basis for their credibility in a variety of settings, including the prediction of drug action. [9][10][11][12][13][14][15][16] This has led to an increase in interest by industry and regulators, as shown by the recent announcement by the Food and Drug Administration of their intentions to replace the Thorough QT study by a combined in vitro/in silico assay. 17 In the next sections, we describe the state-of-the-art in computational cardiac electrophysiology, as an example of an advanced area of in silico biology.…”

Section: Introductionmentioning

confidence: 97%

In Silico Organ Modelling in Predicting Efficacy and Safety of New Medicines

Rodríguez¹

2014

Human-Based Systems for Translational Research

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Over the last 50 years, a number of areas of physiology have experienced great progress in the development and integration of in silico models and simulation tools of different organs. Multiscale human organ computational models are available, for different organs including heart, 1,2 lungs 3-6 and cancer, 7,8 with ne-grained biophysical detail and a great level of integration from the genetic to the whole body level. Multiscale modelling and simulation have been crucial in improving our understanding of mechanisms of physiological and pathological phenomena, therapy and diagnosis. The advanced in silico technology is currently opening up promising and powerful avenues for the investigation and improvement of the efficiency of therapies and for the development of new medicines.Perhaps the most advanced area of computational physiology is computational cardiology, concerned with the investigation of the physiology and pathology of the heart using computational modelling and simulating.

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In vitro–in vivo extrapolation of drug-induced proarrhythmia predictions at the population level

Cited by 23 publications

References 61 publications

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

Model of the Distribution of Diastolic Left Ventricular Posterior Wall Thickness in Healthy Adults and Its Impact on the Behavior of a String of Virtual Cardiomyocytes

In Silico Organ Modelling in Predicting Efficacy and Safety of New Medicines

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