Digital twin for sex-specific identification of class III antiarrhythmic drugs based on in vitro measurements, computer models, and machine learning tools.

Journal: PLoS computational biology
Published Date: Jul 3, 2025

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.

Authors

  • Jieyun Bai
    Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, 510632, China. jbai996@aucklanduni.ac.nz.
  • Weishan Wang
    Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China. Electronic address: wangws@im.ac.cn.
  • Xiaoshen Zhang
    The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangzhou, China.
  • Hua Lu
    Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China.
  • Henggui Zhang
  • Alexander V Panfilov
    Department of Physics and Astronomy, Ghent University, Gent, Belgium.
  • Jichao Zhao
    Auckland Bioengineering Institute, The University of Auckland, Auckland, 1142, New Zealand. Electronic address: j.zhao@auckland.ac.nz.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals