ArticleCirculation research2020
A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm.
Article in Circulation research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed, 82 citations in OpenAlex.
- Arrhythmia risk predictions from molecular simulations of cardiac ion channel-drug interactions.Biophysical journal · 2026Article
- In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.Molecular diversity · 2026Article
- Toward Standardized Methodologies for Drug-Induced Proarrhythmia Classification: An In Silico Proof of Concept.Computational and structural biotechnology journal · 2026Article
- Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG KbioRxiv : the preprint server for biology · 2025Article
- Article
- Computational modelling for improved translation of cardiac inotropic and lusitropic drug effects from rats to humans.Journal of pharmacological and toxicological methods · 2025Article
- Harnessing AlphaFold to reveal hERG channel conformational state secrets.bioRxiv : the preprint server for biology · 2025Article
- Effect of the Membrane Environment on Pharmacologic Inhibition of hERG KJACC. Clinical electrophysiology · 2025Article
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- Article
- An overview of drug-induced sodium channel blockade and changes in cardiac conduction: Implications for drug safety.Clinical and translational science · 2024Review
- Mechanisms of Chemical Atrial Defibrillation by Flecainide and Ibutilide.JACC. Clinical electrophysiology · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Addressing Cardiovascular Toxicity Risk of Electronic Nicotine Delivery Systems in the Twenty-First Century: "What Are the Tools Needed for the Job?" and "Do We Have Them?"Cardiovascular toxicology · 2024Review
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Toward Digital Twin Technology for Precision Pharmacology.JACC. Clinical electrophysiology · 2024Review
- HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A multiscale predictive digital twin for neurocardiac modulation.The Journal of physiology · 2023Article
- Emerging methods to model cardiac ion channel and myocyte electrophysiology.Biophysics reviews · 2023Review
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.