ArticlePLoS computational biology2025
Digital twin for sex-specific identification of class III antiarrhythmic drugs based on in vitro measurements, computer models, and machine learning tools.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives.International journal of pharmaceutics: X · 2025Review
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Atrial fibrillation (AF) significantly affects morbidity and mortality rates. Class III antiarrhythmic drugs (AADs) play a crucial role in managing AF but often exhibit gender-specific complications. Our study aims to identify gender-specific Class III AADs by integrating in vitro measurements, in silico models, and machine learning (ML). By simulating drug effects on a diverse cardiomyocyte model population (5,663 males and 6,184 females), we classified drugs based on changes in action potentials and calcium transients. Using sex-dependent Support Vector Machine (SVM) algorithms, we achieved high prediction accuracy (>89%) and F1 score (>87%). Key features included changes in resting membrane potential and action potential amplitude, duration and area. Gender differences in drug responses were attributed to lower IK1, INa, and Ito in females.
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What Socratic 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 Socratic graph.