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

Abstract

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.

Computational determination of hERG-related cardiotoxicity of drug candidates

“…Classification models for toxicity prediction have been developed using a set of physicochemical descriptors. To improve prediction performance, various machine learning algorithms have been employed, including the support vector machine (SVM), naïve Bayes, decision tree, random forest, and k-nearest neighbors (kNN) [16][17][18][19]. The machine learning algorithms have facilitated the advancement of prediction model development, but the inclusion of inconsistent experimental data included in training datasets damps the development of accurate prediction models [20].…”

Transmural and rate-dependent profiling of drug-induced arrhythmogenic risks through in silico simulations of multichannel pharmacology

“…Multi-scale modelling of the heart has been an important tool in advancing our understanding of cardiac excitation-contraction coupling under both physiological and pathological conditions 15 . Recent pharmacological studies have utilized cardiac cell models for the in silico evaluation of drug-induced proarrhythmic risks 11,16–18 . For example, multiple cardiac ion channels were integrated into the human ventricular cell model to improve the assessment of proarrhythmic risk 19,20 .…”

“…Using in silico human endocardial and Purkinje cell models, Le Guennec et al . evaluated how intra- and inter-individual variability can influence drug-induced proarrhythmic effects of hERG inhibition 16 . In addition, tissue models of the heart have also been implemented to quantitatively evaluate drug-induced risk 22 , e.g.…”