Improving the In Silico Assessment of Proarrhythmia Risk by Combining hERG (Human Ether-à-go-go-Related Gene) Channel-Drug Binding Kinetics and Multichannel Pharmacology

Background: The current proarrhythmia safety testing paradigm, although highly efficient in preventing new torsadogenic drugs from entering the market, has important limitations that can restrict the development and use of valuable new therapeutics. The CiPA (Comprehensive in vitro Proarrhythmia Assay) proposes to overcome these limitations by evaluating drug effects on multiple cardiac ion channels in vitro and using these data in a predictive in silico model of the adult human ventricular myocyte. A set of drugs with known clinical torsade de pointes risk was selected to develop and calibrate the in silico model.

Methods and results: Manual patch-clamp data assessing drug effects on expressed cardiac ion channels were integrated into the O'Hara-Rudy myocyte model modified to include dynamic drug-hERG channel (human Ether-à-go-go-Related Gene) interactions. Together with multichannel pharmacology data, this model predicts that compounds with high torsadogenic risk are more likely to be trapped within the hERG channel and show stronger reverse use dependency of action potential prolongation. Furthermore, drug-induced changes in the amount of electronic charge carried by the late sodium and L-type calcium currents was evaluated as a potential metric for assigning torsadogenic risk.

Conclusions: Modeling dynamic drug-hERG channel interactions and multi-ion channel pharmacology improves the prediction of torsadogenic risk. With further development, these methods have the potential to improve the regulatory assessment of drug safety models under the CiPA paradigm.