ArticleCell stem cell2023
A deep learning platform to assess drug proarrhythmia risk.
Article in Cell stem cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
The trial behind it
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
24 citing papers in PubMed.
- Integrating high-fidelity hiPSC-cardiomyocytes with AI-driven modeling for enhanced proarrhythmic risk assessment.Archives of toxicology · 2026Article
- Article
- BioMADE: Predicting Torsades de Pointes from molecular structures through biologically informed representations.bioRxiv : the preprint server for biology · 2026Article
- Stacking Ensemble Machine Learning for Cardiac Safety Assessment Using hiPSC-CM MEA Data.Annals of biomedical engineering · 2026Article
- A Deep Learning-Based Scoring Framework for Large-Scale Multi-Donor Cardiotoxicity Screening.bioRxiv : the preprint server for biology · 2026Article
- A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.bioRxiv : the preprint server for biology · 2026Article
- Reimagining human-centric drug development with new approach methodologies.Science (New York, N.Y.) · 2026Review
- A machine learning platform for genotype-specific cardiotoxicity risk prediction using patient-derived iPSC-CMs.Journal of advanced research · 2026Article
- Organs-on-Chips in Drug Development: Engineering Foundations, Artificial Intelligence, and Clinical Translation.Biosensors · 2026Review
- A simplified co-culture reveals altered cardiotoxic responses to doxorubicin in hPSC-derived cardiomyocytes in the presence of endothelial cells.Stem cell reports · 2026Article
- A Deep-Learning-Based Scoring Framework for Large-Scale Multi-donor Cardiotoxicity Screening.Computational and structural biotechnology journal · 2026Article
- Biomimetic electrospun scaffolds for engineered heart tissue: from design parameters to drug testing platforms.Frontiers in bioengineering and biotechnology · 2026Review
- The promise of cardiomyocyte computational models for drug safety.Military Medical Research · 2025Article
- Immune and Immune-Integrated Organoids as NextGeneration Platforms for Disease Modeling.MedComm · 2025Review
- Large-scale synthetic data enable digital twins of human excitable cells.bioRxiv : the preprint server for biology · 2025Article
- Human induced pluripotent stem cell-cardiomyocytes for cardiotoxicity assessment: a comparative study of arrhythmiainducing drugs with multi-electrode array analysis.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2025Article
- Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.Annual review of pharmacology and toxicology · 2025Review
- Clinical trials in-a-dish for cardiovascular medicine.European heart journal · 2024Review
- Tackling the challenges of new approach methods for predicting drug effects from model systems.Nature reviews. Drug discovery · 2024Article
- Using induced pluripotent stem cells for drug discovery in arrhythmias.Expert opinion on drug discovery · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinical risk has been much debated. Here, we trained a convolutional neural network classifier (CNN) to learn features of in vitro action potential recordings of hiPSC-CMs that are associated with lethal Torsade de Pointes arrhythmia. The CNN classifier accurately predicted the risk of drug-induced arrhythmia in people. The risk profile of the test drugs was similar across hiPSC-CMs derived from different healthy donors. In contrast, pathogenic mutations that cause arrhythmogenic cardiomyopathies in patients significantly increased the proarrhythmic propensity to certain intermediate and high-risk drugs in the hiPSC-CMs. Thus, deep learning can identify in vitro arrhythmic features that correlate with clinical arrhythmia and discern the influence of patient genetics on the risk of drug-induced arrhythmia.
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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.