ArticleClinical pharmacology and therapeutics2016
Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms.
Article in Clinical pharmacology and therapeutics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
What it found
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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
81 citing papers in PubMed, 143 citations in OpenAlex.
- Alumina nanoparticles-induced reduction in Ito, f contributes to the occurrence of ventricular arrhythmias.BMC cardiovascular disorders · 2026Article
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- BioMADE: Predicting Torsades de Pointes from molecular structures through biologically informed representations.bioRxiv : the preprint server for biology · 2026Article
- T-World Virtual Human Cardiomyocyte. II. Organ-Scale Simulations and Applications.Circulation research · 2026Article
- Proarrhythmic Risk Assessment of Sildenafil under High-Dose Misuse Conditions Using the Comprehensive In Vitro Proarrhythmia Assay (CiPA).Biomolecules & therapeutics · 2026Article
- Beat-to-beat variability of ventricular repolarization reveals sex-specific instability and proarrhythmic risk.The Journal of physiology · 2026Article
- Population Modeling Approach for Human Cardiac Arrhythmia Risk Prediction.Circulation. Arrhythmia and electrophysiology · 2026Article
- A multiscale computational model of cardiac electrophysiology for drug-induced pro-arrhythmic risk stratification.Biomedical engineering letters · 2026Article
- Toward Standardized Methodologies for Drug-Induced Proarrhythmia Classification: An In Silico Proof of Concept.Computational and structural biotechnology journal · 2026Article
- 25 years of applying the 3Rs principles in safety pharmacology: success stories and future perspectives.Frontiers in physiology · 2026Review
- Development and Clinical Validation of a Cross-Sex Translator of ECG Drug Responses.JACC. Clinical electrophysiology · 2025Article
- Validation of new AI-based classification method for in silico cardiac safety assessment of drugs following the CiPA framework.Archives of toxicology · 2025Article
- Digital twin for sex-specific identification of class III antiarrhythmic drugs based on in vitro measurements, computer models, and machine learning tools.PLoS computational biology · 2025Article
- Article
- A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers.CPT: pharmacometrics & systems pharmacology · 2024Article
- Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation.Scientific reports · 2024Article
- Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORdFrontiers in physiology · 2024Article
- Assessing drug safety by identifying the axis of arrhythmia in cardiomyocyte electrophysiology.eLife · 2023Article
- Estimating the probability of early afterdepolarizations and predicting arrhythmic risk associated with long QT syndrome type 1 mutations.Biophysical journal · 2023Article
- Prediction of anesthetic torsadogenicity using a human ventricular cell model.Journal of anesthesia · 2023Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug-induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor specificity and a lack of mechanistic insight. We addressed this important unresolved issue through a novel computational approach that combined simulations of drug effects on dynamics with statistical analysis and machine-learning. Drugs that blocked multiple ion channels were simulated in ventricular myocyte models, and metrics computed from the action potential and intracellular (Ca(2+) ) waveform were used to construct classifiers that distinguished between arrhythmogenic and nonarrhythmogenic drugs. We found that: (1) these classifiers provide superior risk prediction; (2) drug-induced changes to both the action potential and intracellular (Ca(2+) ) influence risk; and (3) cardiac ion channels not typically assessed may significantly affect risk. Our algorithm demonstrates the value of systematic simulations in predicting pharmacological toxicity.
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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.