Biomed Res Int. 2026;2026(1):e4900599. doi: 10.1155/bmri/4900599.
ABSTRACT
Brugada syndrome (BrS) is a rare but potentially fatal genetic cardiac disorder, primarily caused by mutations in sodium and potassium ion channels, leading to ventricular arrhythmias and sudden cardiac death. Despite advances in computational modeling, the precise effects of ionic environment and channel kinetics on BrS-related action potentials remain incompletely understood. In this study, we developed a flexible electrophysiological model of cardiac sodium channels using a modifiable Richards activation function to simulate major BrS phenotypes and optimized the shape parameter (h) using particle swarm optimization (PSO). In parallel, molecular dynamics (MD) simulations were performed to analyze Na+ ion behavior in the selectivity filter under varying ionic conditions, providing molecular-level insights into channel function. Results revealed that tuning the h parameter significantly improved key features of the action potential, including amplitude (APA), duration (APD), and time to peak (t_peak), aligning them more closely with physiological profiles. The protein structure was simulated in a solvated box using the AMBER03 force field under transmembrane potentials of 100-200 mV and at an ionic strength of 0.14 M. RMSD analysis confirmed greater structural stability of the protein in the presence of ionic strength; however, the additional ions created localized electric fields that initially disrupted ion flux. Increasing the applied voltage and cutoff radius to 1.4 nm reactivated ion transport, reproducing the "knock-on" mechanism. Sensitivity analysis indicated that certain models exhibited stronger responses to changes in the activation function, highlighting their suitability for personalized modeling. The proposed model, without altering the fundamental channel structure, successfully simulates genetic dysfunctions by adjusting a single key parameter. It provides a practical framework for simulation-based analysis of sodium channel dysfunction, arrhythmia risk assessment, and the exploration of personalized cardiac modeling strategies.
PMID:42458768 | PMC:PMC13373304 | DOI:10.1155/bmri/4900599