Arrhythmia risk predictions from molecular simulations of cardiac ion channel-drug interactions
Kyle C Rouen1, Kush Narang1, Yanxiao Han2
1Department of Physiology and Membrane Biology, University of California, Davis, Davis, California.
Insights
Drug interactions with cardiac ion channels like hERG can cause deadly arrhythmia. This study used computational modeling and machine learning to predict drug binding affinities and assess arrhythmia risk, aiding safer drug development.
Area of Science:
- Computational chemistry
- Pharmacology
- Cardiovascular research
Background:
- Unintended blockade of cardiac ion channels, especially hERG (KV11.1), is a major safety concern in drug development, potentially causing fatal arrhythmias.
- Assessing the proarrhythmic risk of drug candidates requires understanding their interactions with key cardiac ion channels.
Purpose of the Study:
- To investigate drug interactions with hERG (KV11.1), NaV1.5, and CaV1.2 channels in different functional states.
- To develop and validate a computational method for predicting drug binding affinities and assessing arrhythmia risk.
Main Methods:
- Utilized Rosetta structural modeling to create models of cardiac ion channels in open and inactivated states based on cryo-EM structures.
- Employed site identification by ligand competitive saturation (SILCS), a physics-based docking method, to predict drug binding affinities.
- Applied Bayesian machine learning to refine SILCS scoring using experimental data and to classify drug arrhythmia risk.
Main Results:
- The SILCS method, refined with machine learning, outperformed other docking tools in predicting drug binding affinities for hERG.
- Computed binding affinities for hERG and CaV1.2 channels were used to train models that successfully classified ~300 drugs.
- SILCS fragment free energy scores of cationic nitrogens were identified as key predictors of drug-induced torsades de pointes arrhythmia risk.
Conclusions:
- The developed computational approach accurately predicts drug binding to cardiac ion channels and assesses arrhythmia risk.
- This method, relying on predicted binding free energies and physical properties, offers a promising strategy for early-stage drug design to mitigate proarrhythmic potential.
Abstract:
Unintended block of cardiac ion channels, particularly hERG (KV11.1), remains a key concern in drug development as disruption of ion channel function can lead to deadly arrhythmia. To assess proarrhythmic risk, we investigated how drugs interact with hERG in its open and inactivated states and whether drug interactions with other cardiac channels like NaV1.5 and CaV1.2 mitigate that risk. Using cryo-EM structures, we modeled open and inactivated conformations of these channels with Rosetta structural modeling. We then applied SILCS (site identification by ligand competitive saturation), a physics-based precomputed ensemble docking method, to predict drug binding affinities. SILCS leverages molecular-simulation-generated free energy maps for high-throughput docking against hydrated lipid bilayer-embedded ion channel models. Bayesian machine learning was used to refine SILCS scoring using experimental IC50 values from 53 known hERG blockers outperforming Schrödinger Glide, AutoDock Vina, and OpenEye FRED drug docking predictions. Computed drug binding affinities for hERG and CaV1.2 channels were used to train machine learning models that successfully classified around 300 drugs from the CredibleMeds database. Cationic nitrogen SILCS fragment free energy scores were found to be top physical properties that are predictive of drug-induced torsades de pointes arrhythmia risk. This approach, which relies on the predicted binding free energies and predicted physical properties of drugs rather than the chemical structure of the drugs themselves as features, could be extended to facilitate the design of new drugs where rapid assessment of arrhythmia risk can be performed before experimental testing.
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