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HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety

Inass Soukarieh1,2, Gerhard Hessler3, Hervé Minoux1

  • 1Sanofi, Digital R&D, Vitry-Sur-Seine, France.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 30, 2026
PubMed
Summary

A new hyperSBINN model uses meta-learning and neural networks to speed up cardiac action potential (CAP) predictions for drug discovery. This computational tool efficiently models drug effects on the heart, aiding preclinical development.

Keywords:
cardiac action potentialhypernetworksystem biology-informed neural network

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Area of Science:

  • Computational toxicology
  • Systems biology
  • Pharmacology

Background:

  • Mathematical models of cardiac action potentials (CAPs) are crucial for understanding drug effects on cardiac health.
  • Model complexity often hinders their use in early drug discovery.
  • Efficient computational tools are needed to predict compound effects on cardiac electrophysiology.

Purpose of the Study:

  • To introduce a novel method for solving parameterized CAP models using meta-learning and systems biology-informed neural networks (SBINNs).
  • To develop a computational framework, hyperSBINN, for rapid and accurate prediction of drug effects on CAPs.
  • To enhance the application of systems toxicology in preclinical drug development.

Main Methods:

  • Developed hyperSBINN, a hybrid approach combining meta-learning with SBINNs.
  • Applied the model to predict the effects of various compounds at different concentrations on CAPs.
  • Evaluated model performance against traditional differential equation solvers, focusing on speed and accuracy in predicting APD90 values.

Main Results:

  • The hyperSBINN model significantly outperforms traditional differential equation solvers in terms of speed.
  • The model demonstrates robust performance in predicting APD90 values, even with limited data.
  • Successfully handles complex parameterized differential equations relevant to cardiac electrophysiology.

Conclusions:

  • hyperSBINN offers a scalable and efficient solution for modeling cardiac electrophysiology.
  • This advancement in computational modeling aids in understanding complex biological systems and supports preclinical drug development.
  • The approach shows promise as a reliable tool for predicting drug-induced cardiotoxicity.