Dynamic hERG models enhance cardiac safety assessment

Cardiac toxicity testing is crucial in the drug development process to ensure the safety of new pharmaceutical compounds. The Comprehensive in-vitro Pro-arrhythmia Assay (CiPA) initiative aims to improve the prediction of drug-induced arrhythmias by integrating multiple data sources, including hERG channel assays, to assess the proarrhythmic risk of drugs.

Given the risks associated with hERG inhibition, it is standard practice in the pharmaceutical industry to screen new drug candidates for their potential to inhibit the hERG channel. However, traditional assessments, such as IC50 measurements, exhibit high variability under different experimental conditions.

Recent advancements have indicated that modeling drug binding dynamics to hERG can enhance the early assessment of cardiotoxicity. Meanwhile, existing CiPA models do not fully capture the complexities of drug-hERG interactions. Therefore, there is a need for more accurate dynamic models that better reflect the state-dependent binding properties and trapping dynamics of drugs.

A recent study presented an experimentally validated methodology for creating dynamic models of drug-hERG channel interactions.

The researchers employed a high-throughput automated platform, SyncroPatch 384, to conduct experiments on HEK cells expressing hERG channels. They applied three different voltage-clamp protocols (P80, P0, and P40) to gather experimental data. Using this data, they generated dynamic Markovian models for ten known IKr blockers. These models were then employed to simulate APD prolongation and evaluate the impact of dynamic versus static drug interactions.

The experimental results revealed a wide range of drug responses with significant protocol-dependent variations in some cases. The dynamic models accurately reflected these experimental outcomes, unlike the CiPA models. These models successfully demonstrated the importance of state-dependent binding and trapping dynamics in predicting drug effects on IKr and APD.

The dynamic models also showed notable differences in APD prolongation compared to static models, highlighting the critical role of drug dynamics in assessing cardiotoxicity.

The CiPA dynamic models did not reproduce the protocol-dependence of IC50 values observed in the experiments, highlighting the superiority of the newly developed dynamic models in capturing the preferential state-dependent binding properties of drugs.

Overall, these findings suggest that incorporating drug dynamics is essential for accurately predicting APD prolongation and potential proarrhythmic risks, offering a more reliable approach than current CiPA models.

Find the full article here: https://www.sciencedirect.com/science/article/pii/S0169260724002888

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