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Published on: February 8, 2011
Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and
Fernando Escobar1, Soren Friis2, Nouran Adly2
1Centro de Innovación e Investigación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain.
Insights
This study validates a new dynamic modeling method for assessing drug cardiotoxicity by accurately predicting drug interactions with the hERG channel. The findings emphasize the importance of dynamic modeling for understanding drug effects on cardiac action potentials.
Area of Science:
- Pharmacology
- Computational Biology
- Cardiovascular Research
Background:
- Drug-induced cardiotoxicity is a major concern in pharmaceutical development.
- Modeling drug-binding dynamics to the hERG channel aids in early cardiotoxicity assessment.
- Previous work established a methodology for Markovian models of drug-hERG interactions.
Purpose of the Study:
- To validate a previously developed Markovian modeling methodology using real IKr blockers.
- To investigate the impact of drug dynamics on action potential prolongation.
- To compare dynamic models with static models and existing CiPA dynamic models.
Main Methods:
- HEK cells stably transfected with hERG were used with the Nanion SyncroPatch 384i.
- Three voltage clamp protocols (P-80, P0, P40) were applied to collect experimental data.
- Markovian and static models were generated; action potential duration was simulated using a modified O'Hara-Rudy model.
Main Results:
- Experimental data for ten IKr blockers, including Hill plots and onset of IKr block, were obtained.
- The generated dynamic Markovian models successfully mimicked experimental data, outperforming CiPA dynamic models.
- Simulations revealed significant differences in action potential duration prolongation between dynamic and static models.
Conclusions:
- The study validates a methodology for modeling dynamic drug-hERG channel interactions.
- State-dependent binding, trapping dynamics, and the time-course of IKr block are crucial for assessing drug effects.
- Dynamic models are essential for accurately predicting drug-induced changes in cardiac electrophysiology.
Background And Objective:
Assessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation.
Methods:
Experiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration.
Results:
The experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models.
Conclusions:
These new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug-hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.
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