Inter-individual variability and modeling of electrical activity: a possible new approach to explore cardiac safety?

被引:5
作者
Le Guennec, Jean-Yves [1 ]
Thireau, Jerome [1 ]
Ouille, Aude [1 ]
Roussel, Julien [1 ]
Roy, Jerome [1 ]
Richard, Serge [2 ]
Richard, Sylvain [1 ]
Martel, Eric [2 ,3 ]
Champeroux, Pascal [2 ]
机构
[1] Univ Montpellier, INSERM, CNRS, U1046,UMR 9214, 371 Ave Doyen Gaston Giraud, F-34295 Montpellier, France
[2] CERB, Ctr Rech Biol, Chemin Montifault, F-18800 Baugy, France
[3] Boehringer Ingelheim Pharma GmbH & Co KG, D-88397 Biberach, Germany
关键词
QT INTERVAL PROLONGATION; TORSADE-DE-POINTES; PARAMETER SENSITIVITY-ANALYSIS; DRUG CARDIOTOXICITY; K+ CURRENT; RISK; REPOLARIZATION; HEART; ELECTROPHYSIOLOGY; ARRHYTHMIAS;
D O I
10.1038/srep37948
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Safety pharmacology aims to predict rare side effects of new drugs. We explored whether rare pro-arrhythmic effects could be linked to the variability of the effects of these drugs on ion currents and whether taking into consideration this variability in computational models could help to better detect and predict cardiac side effects. For this purpose, we evaluated how intra-and inter-individual variability influences the effect of hERG inhibition on both the action potential duration and the occurrence of arrhythmias. Using two computer simulation models of human action potentials (endocardial and Purkinje cells), we analyzed the contribution of two biological parameters on the pro-arrhythmic effects of several hERG channel blockers: (i) spermine concentration, which varies with metabolic status, and (ii) L-type calcium conductance, which varies due to single nucleotide polymorphisms or mutations. By varying these parameters, we were able to induce arrhythmias in 1 out of 16 simulations although conventional modeling methods to detect pro-arrhythmic molecules failed. On the basis of our results, taking into consideration only 2 parameters subjected to intra-and inter-individual variability, we propose that in silico computer modeling may help to better define the risks of new drug candidates at early stages of pre-clinical development.
引用
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页数:13
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