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

Abstract

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.