Integration of electrophysiological and mechanical biomarkers in cardiac risk assessment models

Cardiac drug safety assessment is essential to detect molecules with potential adverse effects, particularly those increasing the risk of arrhythmias such as Torsade de Pointes (TdP). The challenge lies in achieving early predictions with minimal experimental studies while maintaining high sensitivity and specificity. Traditional approaches rely primarily on electrophysiological (EP) biomarkers; however, mechanical effects of drugs on cardiac contractility remain underexplored. This study aims to integrate electrophysiological and mechanical biomarkers to improve cardiac risk assessment models.

The present study investigated the integration of electrophysiological and mechanical biomarkers using a cellular and three-dimensional in silico population approach. We evaluated the effects on different EP and mechanical relevant biomarkers to assess both, the proarrhythmic and inotropic risks of 39 compounds, including CiPA compounds and calcium channel blockers (CCBs). Classification models were developed using EP biomarkers alone and in combination with mechanical biomarkers to evaluate their predictive capabilities.

Classification models based solely on EP biomarkers demonstrated robust prediction on CiPA torsadogenic risk. The inclusion of mechanical biomarkers did not enhance classification accuracy for TdP risk. However, mechanical metrics revealed significant contractile changes induced by CCBs and other negative inotropic compounds, such as mavacamten. Drugs induced a range of fractional shortening values, correlated with ejection fraction variations, highlighting clinically relevant contractile effects.

EP-based assessments remain reliable for predicting torsadogenic risk, but mechanical biomarkers provide crucial insights into drug-induced cardiac contractile effects. Future studies should focus on increasing experimental data availability and incorporating more complex cardiac geometries to enhance translational applicability of in silico models in comprehensive drug safety evaluation.