Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Sep 12;8:668.
doi: 10.3389/fphys.2017.00668. eCollection 2017.

Human In Silico Drug Trials Demonstrate Higher Accuracy Than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Affiliations
Free PMC article

Human In Silico Drug Trials Demonstrate Higher Accuracy Than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Elisa Passini et al. Front Physiol. .
Free PMC article

Abstract

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na+ and Ca2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca2+-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Keywords: Torsade de Pointes; computer models; drug cardiotoxicity; drug safety; human ventricular action potential; in silico drug trials.

Figures

Figure 1
Figure 1
Explanatory examples of in silico drug trial results in a population of human computational models, showing drug-induced changes on APD90 and dV/dtMAX (left and right column, respectively) for 5 compounds (Dofetilide I, Flecainide I, Nimodipine, Ranolazine I and Verapamil II). Results are presented as boxplots of AP biomarkers for the population of human ventricular models at increasing concentrations (A–J). Results for the single ORd model are shown as black diamonds. On each box, the central mark is the median of the population, box limits are the 25 and 75th percentiles, and whiskers extend to the most extreme data points not considered outliers, plotted individually as separate crosses. Extended results for the selected 15 reference compounds, including all the AP biomarkers, are available in the Supplementary Material, Figures S3–S31.
Figure 2
Figure 2
Explanatory examples of variability in drug response in the in silico population of human AP models, with the underlying ionic mechanisms. Representative AP traces of different drug-induced AP phenotypes are shown on the left side for Moxifloxacin III (A), Dofetilide I (B), and Flecainide I (C) at selected concentrations. Models with a normal AP are shown in gray, while models displaying RA and DA are shown in pink and green, respectively. In each panel, the baseline ORd model is highlighted in black. The distribution of ionic conductances for the different AP phenotypes is shown on the right side (D–F), by using boxplots of the corresponding scaling factors, and with the same color code. For each conductance, the values shown (between 0 and 2) represent the scaling factors of the models in the population compared to the baseline ORd model, which had all the scaling factors equal to 1. Boxplots description as in Figure 1.
Figure 3
Figure 3
In silico prediction of in vivo TdP risk for the 49 compounds belonging to TdP risk category 0 and 1 (A) and for all the 62 tested compounds (B). In each panel, predictions based on the occurrence of RA in any of the model at 1x, 10x, 30x, and 100x EFTPCmax (1st column) are compared against predictions based on APD90 prolongation >6% at 10x EFTPCmax (2nd column). Results obtained using the population of models (top half) are compared against the ones for the baseline ORd model (bottom half). High sensitivity/specificity/accuracy (>80%) are highlighted in bold.
Figure 4
Figure 4
TdP score for all the 62 compounds, based on the occurrence of RA in the population of human AP models at 1x, 10x, 30x, and 100x EFTPCmax. The TdP score, which varies between 0 and 1, was computed by taking into account both the fraction of models displaying RA and the concentrations at which RA occur, as described in Methods. The logarithmic scale was considered to maximize the visual separation between safe and risky drugs, and log10(0) was approximated with the machine precision (10−16). All compounds with no report of TdP risk (in green) have 0 as TdP score (left side), except for Lidocaine and Mexiletine. All high risk compounds (in red) have a high TdP score (right side). Most of the compounds with possible or conditional TdP risk (in orange and yellow, respectively) have a TdP score >0, except for Paroxetine, Voriconazole, Clozapine, Dasatinib, and Saquinavir.
Figure 5
Figure 5
Qualitative and quantitative comparison of in silico drug trial results in the population of human ventricular AP models against ECG from rabbit wedge preparations and Ca2+ transient recordings from hiPS-CMs, for 15 reference compounds. On the left side (from red to green) are shown the drug-induced changes in the biomarkers related to the repolarization phase: QT interval in rabbit wedge, CTD90 in hiPS-CMs and APD90 in silico. On the right side (from purple to blue) are shown the drug-induced changes in the biomarkers related to the depolarization phase: QRS interval from rabbit wedge and dV/dtMAX in silico. For each assay, colors are scaled to span from the 15th to the 85th percentiles of the % changes observed in the biomarkers when considering drug effects, compared to no drug, and with respect to the cut-off values (3 and 5% for rabbit wedge QRS and QT, 25% for hiPS-CMs CTD90, and 0% for in silico APD90 and dV/dtMAX). To facilitate comparison, negative variations in dV/dtMAX were considered as positive changes in the depolarization time, and vice versa. When multiple combinations of IC50 and h were tested in simulation for the same compound, the corresponding in silico result sections consist of multiple sub-columns. Statistically significant changes in experiments have been highlighted in bold.

Similar articles

See all similar articles

Cited by 38 articles

See all "Cited by" articles

References

    1. Abbasi M., Small B. G., Patel N., Jamei M., Polak S. (2017). Early assessment of proarrhythmic risk of drugs using the in vitro data and single-cell-based in silico models: proof of concept. Toxicol. Mech. Methods 27, 88–99. 10.1080/15376516.2016.1256460 - DOI - PubMed
    1. Abi-Gerges N., Pointon A., Oldman K. L., Brown M. R., Pilling M. A., Sefton C. E., et al. . (2017). Assessment of extracellular field potential and Ca2+ transient signals for early QT/pro-arrhythmia detection using human induced pluripotent stem cell-derived cardiomyocytes. J. Pharmacol. Toxicol. Methods 83, 1–15. 10.1016/j.vascn.2016.09.001 - DOI - PubMed
    1. Bányász T., Bárándi L., Harmati G., Virág L., Szentandrássy N., Márton I., et al. . (2011). Mechanism of reverse rate-dependent action of cardioactive agents. Curr. Med. Chem. 18, 3597–3606. 10.2174/092986711796642355 - DOI - PubMed
    1. Bass A. S., Hombo T., Kasai C., Kinter L. B., Valentin J.-P. (2015). A historical view and vision into the future of the field of safety pharmacology, in Principles of Safety Pharmacology, Handbook of Experimental Pharmacology Vol. 229, eds Pugsley M. K., Curtis M. J., editors. (Berlin; Heidelberg: Springer; ), 3–45. 10.1007/978-3-662-46943-9_1 - DOI - PubMed
    1. Beattie K. A., Luscombe C., Williams G., Munoz-Muriedas J., Gavaghan D. J., Cui Y., et al. . (2013). Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge. J. Pharmacol. Toxicol. Methods 68, 88–96. 10.1016/j.vascn.2013.04.004 - DOI - PMC - PubMed

LinkOut - more resources

Feedback