Search

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel - drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel - drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking.

RationaleDrug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery.ObjectiveTo predict the impact of a drug from the drug chemistry on the cardiac rhythm.Methods and resultsIn a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction.ConclusionsWe present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

Interactions of drug molecules with lipid membranes play crucial role in their accessibility of cellular targets and can be an important predictor of their therapeutic and safety profiles. Very little is known about spatial localization of various drugs in the lipid bilayers, their active form (ionization state) or translocation rates and therefore potency to bind to different sites in membrane proteins. All-atom molecular simulations may help to map drug partitioning kinetics and thermodynamics, thus providing in-depth assessment of drug lipophilicity. As a proof of principle, we evaluated extensively lipid membrane partitioning of d-sotalol, well-known blocker of a cardiac potassium channel Kv11.1 encoded by the hERG gene, with reported substantial proclivity for arrhythmogenesis. We developed the positively charged (cationic) and neutral d-sotalol models, compatible with the biomolecular CHARMM force field, and subjected them to all-atom molecular dynamics (MD) simulations of drug partitioning through hydrated lipid membranes, aiming to elucidate thermodynamics and kinetics of their translocation and thus putative propensities for hydrophobic and aqueous hERG access. We found that only a neutral form of d-sotalol accumulates in the membrane interior and can move across the bilayer within millisecond time scale, and can be relevant to a lipophilic channel access. The computed water-membrane partitioning coefficient for this form is in good agreement with experiment. There is a large energetic barrier for a cationic form of the drug, dominant in water, to cross the membrane, resulting in slow membrane translocation kinetics. However, this form of the drug can be important for an aqueous access pathway through the intracellular gate of hERG. This route will likely occur after a neutral form of a drug crosses the membrane and subsequently re-protonates. Our study serves to demonstrate a first step toward a framework for multi-scale in silico safety pharmacology, and identifies some of the challenges that lie therein.

The intra-cavitary drug blockade of hERG1 channel has been extensively studied, both experimentally and theoretically. Structurally diverse ligands inadvertently block the hERG1 K + channel currents lead to drug induced Long QT Syndrome (LQTS). Accordingly, designing either hERG1 channel openers or current activators, with the potential to target other binding pockets of the channel, has been introduced as a viable approach in modern anti-arrhythmia drug development. However, reports and investigations on the molecular mechanisms underlying activators binding to the hERG1 channel remain sparse and the overall molecular design principles are largely unknown. Most of the hERG1 activators were discovered during mandatory screening for hERG1 blockade. To fill this apparent deficit, the first universal pharmacophore model for hERG1 K + channel activators was developed using PHASE. 3D structures of 18 hERG1 K + channel activators and their corresponding measured binding affinity values were used in the development of pharmacophore models. These compounds spanned a range of structurally different chemotypes with moderate variation in binding affinity. A five sites AAHRR (A, hydrogen-bond accepting, H, hydrophobic, R, aromatic) pharmacophore model has shown reasonable high statistical results compared to the other developed more than 1000 hypotheses. This model was used to construct steric and electrostatic contour maps. The predictive power of the model was tested with 3 external test set compounds as true unknowns. Finally, the pharmacophore model was combined with the previously developed receptor-based model of hERG1 K + channel to develop and screen novel activators. The results are quite striking and it suggests a greater future role for pharmacophore modeling and virtual drug screening simulations in deciphering complex patterns of molecular mechanisms of hERG1 channel openers at the target sites. The developed model is available upon request and it may serve as basis for the synthesis of novel therapeutic hERG1 activators. [ABSTRACT FROM AUTHOR]

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K + ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model. [ABSTRACT FROM AUTHOR]