Elevating performance and interpretability of in silico classifiers for drug proarrhythmia risk evaluations using multi-biomarker approach with ranking algorithm.

Background And Objective: Using electrophysiological simulations and machine learning to predict drug proarrhythmia risk has gained popularity due to its effectiveness. The leading in silico drug assessment system mainly uses a single biomarker (qNet) to predict proarrhythmia risk, offering good performance and straightforward interpretation. Other advanced classifiers incorporating additional physiological biomarkers provide better predictive capabilities but are less intuitive. Thus, a method that accommodates multiple biomarkers while maintaining interpretability is needed.

Methods: We enhance the current best ordinal logistic regression (OLR) model by adding more physiological biomarkers to overcome its limitations. We also introduce a general torsade metric score (TMS) for multi-biomarker approaches to facilitate easier interpretation. Additionally, a novel ranking algorithm based on a simple multi-criteria decision analysis method is employed to evaluate various classifiers against standard proarrhythmia risk criteria efficiently.

Results: Our proposed method demonstrates that using multiple well-known biomarkers yields better performance than using qNet alone. Some accepted multi-biomarker OLR models do not incorporate qNet yet outperform those that do. Moreover, some ill-performing biomarkers when utilized individually can show improved performance in combination with other biomarkers.

Conclusion: The proposed approach offers an effective way of utilizing multiple biomarkers, including well-known ones, providing practical alternatives for proarrhythmia risk assessment. The interpretability of the accepted models is straightforward, thanks to the TMS thresholds for multi-biomarker OLR models that allow direct evaluation of the classification prediction of individual drugs.