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arXiv · 2408.14266

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

Inass Soukarieh·Gerhard Hessler·Hervé Minoux·Marcel Mohr·Friedemann Schmidt·Jan Wenzel·Pierre Barbillon·Hugo Gangloff·Pierre Gloaguen
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Abstract

Mathematical modeling in systems toxicology enables a comprehensive understanding of the effects of pharmaceutical substances on cardiac health. However, the complexity of these models limits their widespread application in early drug discovery. In this paper, we introduce a novel approach to solving parameterized models of cardiac action potentials by combining meta-learning techniques with Systems Biology-Informed Neural Networks (SBINNs). The proposed method, hyperSBINN, effectively addresses the challenge of predicting the effects of various compounds at different concentrations on cardiac action potentials, outperforming traditional differential equation solvers in speed. Our model efficiently handles scenarios with limited data and complex parameterized differential equations. The hyperSBINN model demonstrates robust performance in predicting APD90 values, indicating its potential as a reliable tool for modeling cardiac electrophysiology and aiding in preclinical drug development. This framework represents an advancement in computational modeling, offering a scalable and efficient solution for simulating and understanding complex biological systems.

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BibTeXRIS

Inass Soukarieh, Gerhard Hessler, Hervé Minoux, Marcel Mohr, Friedemann Schmidt, Jan Wenzel, Pierre Barbillon, Hugo Gangloff, Pierre Gloaguen. 2024-08-26. HyperSBINN: A Hypernetwork-Enhanced Systems Biology-Informed Neural Network for Efficient Drug Cardiosafety Assessment. https://arxiv.org/abs/2408.14266

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