Key result
Incorporating inter-individual variability in spermine concentration and L-type calcium conductance into in silico models induced arrhythmias in 1 out of 16 simulations, detecting pro-arrhythmic risks missed by conventional methods.
Why the study?
Does incorporating intra- and inter-individual variability in computational models improve the detection of pro-arrhythmic effects of hERG channel blockers?
Does incorporating intra- and inter-individual variability in computational models improve the detection of pro-arrhythmic effects of hERG channel blockers?
Incorporating intra- and inter-individual variability in in silico computer modeling may improve the early detection of pro-arrhythmic risks of new drug candidates.
May enhance in silico pro-arrhythmia screening; hypothesis-generating and requires prospective validation before clinical use.
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.
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Guennec et al. (2016) studied Drug-induced arrhythmias (n=16). In silico modeling incorporating inter-individual variability (spermine concentration and L-type calcium conductance) vs. Conventional modeling methods was evaluated on Induction of arrhythmias (Early After Depolarizations). Incorporating inter-individual variability in spermine concentration and L-type calcium conductance into in silico models induced arrhythmias in 1 out of 16 simulations, detecting pro-arrhythmic risks missed by conventional methods.
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