ArticleFrontiers in pharmacology2017
Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features.
Article in Frontiers in pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 42 citations in OpenAlex.
- Improving In Silico Cardiac Safety Prediction by Consensus Averaging of Transmural Ventricular Cell Models.Annals of biomedical engineering · 2026Article
- 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
- Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.Archives of toxicology · 2023Article
- Application of Convolutional Neural Networks Using Action Potential Shape for In-Silico Proarrhythmic Risk Assessment.Biomedicines · 2023Article
- Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.Frontiers in physiology · 2023Article
- Proarrhythmic risk assessment of drugs by dVCPT: pharmacometrics & systems pharmacology · 2022Article
- Prediction of arrhythmia susceptibility through mathematical modeling and machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Human-induced pluripotent stem cell-derived cardiomyocytes, 3D cardiac structures, and heart-on-a-chip as tools for drug research.Pflugers Archiv : European journal of physiology · 2021Review
- Machine Learning in Arrhythmia and Electrophysiology.Circulation research · 2021Review
- Assessment of Drug Proarrhythmicity Using Artificial Neural Networks WithFrontiers in physiology · 2021Article
- Review
- All-Optical Electrophysiology Refines Populations of In Silico Human iPSC-CMs for Drug Evaluation.Biophysical journal · 2020Article
- Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning.Biophysical journal · 2020Article
- General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy.Clinical pharmacology and therapeutics · 2020Article
- Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator.Progress in biophysics and molecular biology · 2019Article
- Quantitative Systems Pharmacology Models for a New International Cardiac Safety Regulatory Paradigm: An Overview of the Comprehensive In Vitro Proarrhythmia Assay In Silico Modeling Approach.CPT: pharmacometrics & systems pharmacology · 2019Review
- Usefulness of Bnet, a Simple Linear Metric in Discerning Torsades De Pointes Risks in 28 CiPA Drugs.Frontiers in pharmacology · 2019Article
- Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment.Frontiers in pharmacology · 2019Article
- BlindedFrontiers in pharmacology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While pre-clinical Torsades de Pointes (TdP) risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features). Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the
Indexed as
Identifiers
What OpenQuestion holds
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.