Toxicology and applied pharmacology · 2026
Improving biomarker robustness for in silico cardiac safety assessment through an excitation-contraction coupling model.
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Hanum Ulfa Latifa, Qauli Ali Ikhsanul, Ariyadi, Lim Ki Moo
Аннотация
BACKGROUND AND OBJECTIVE: Predicting drug-induced cardiac toxicity remains a critical challenge in preclinical safety assessment, particularly for evaluating the risk of Torsade de Pointes (TdP). While the Comprehensive in vitro Proarrhythmia Assay (CiPA) framework enables biomarker-based risk stratification, the robustness of these biomarkers under ion-channel uncertainty remains insufficiently characterized. This study aims to systematically evaluate whether incorporating excitation-contraction (EC) coupling can improve biomarker robustness for TdP risk assessment.
METHODS: Human ventricular electrophysiological models (CiPAORdv1.0 and ToR-ORd) were integrated with a Land-based electromechanical model. Eleven biomarkers were evaluated using ordinal logistic regression, with 12 drugs used for training and 16 drugs for testing. To account for uncertainty, model performance was assessed using 10,000 independent test-time iterations following the CiPA framework.
RESULTS: Integration of EC coupling consistently increased the number of accepted biomarkers (CiPAORdv1.0: 4 to 6; ToR-ORd: 5 to 7). The most pronounced improvements were observed in calcium transient-based biomarkers (CaTD50, CaTD90), which transitioned from rejected to accepted. In addition, voltage-based biomarkers such as APD90 and qNet exhibited reduced variability and improved statistical stability, as reflected by narrower confidence intervals.
CONCLUSIONS: These findings demonstrate that incorporating electromechanical coupling enhances biomarker robustness by improving the physiological representation of calcium dynamics under uncertain conditions. This study provides a structured computational evaluation of biomarker reliability and highlights the potential of electromechanical models to strengthen in silico TdP risk prediction within the CiPA framework.
METHODS: Human ventricular electrophysiological models (CiPAORdv1.0 and ToR-ORd) were integrated with a Land-based electromechanical model. Eleven biomarkers were evaluated using ordinal logistic regression, with 12 drugs used for training and 16 drugs for testing. To account for uncertainty, model performance was assessed using 10,000 independent test-time iterations following the CiPA framework.
RESULTS: Integration of EC coupling consistently increased the number of accepted biomarkers (CiPAORdv1.0: 4 to 6; ToR-ORd: 5 to 7). The most pronounced improvements were observed in calcium transient-based biomarkers (CaTD50, CaTD90), which transitioned from rejected to accepted. In addition, voltage-based biomarkers such as APD90 and qNet exhibited reduced variability and improved statistical stability, as reflected by narrower confidence intervals.
CONCLUSIONS: These findings demonstrate that incorporating electromechanical coupling enhances biomarker robustness by improving the physiological representation of calcium dynamics under uncertain conditions. This study provides a structured computational evaluation of biomarker reliability and highlights the potential of electromechanical models to strengthen in silico TdP risk prediction within the CiPA framework.