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

Qauli, AI, Mahardika T, NQ, Hanum, UL, Vanheusden, FJ ORCID: https://orcid.org/0000-0003-2369-6189 and Lim, KM, 2025. Elevating performance and interpretability of in silico classifiers for drug proarrhythmia risk evaluations using multi-biomarker approach with ranking algorithm. Computer Methods and Programs in Biomedicine, 261: 108609. ISSN 0169-2607

Text 2356017_Vanheusden.pdf - Post-print Full-text access embargoed until 17 January 2026. Download (2MB)

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.

Item Type:	Journal article
Publication Title:	Computer Methods and Programs in Biomedicine
Creators:	Qauli, A.I., Mahardika T, N.Q., Hanum, U.L., Vanheusden, F.J. and Lim, K.M.
Publisher:	Elsevier BV
Date:	April 2025
Volume:	261
ISSN:	0169-2607
Identifiers:	Number Type 10.1016/j.cmpb.2025.108609 DOI 2356017 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	06 Feb 2025 16:16
Last Modified:	06 Feb 2025 16:16
URI:	https://irep.ntu.ac.uk/id/eprint/52979

Actions (login required)

Edit View

Statistics

Views

Downloads