Drug induced TdP risks classification assay using electro-mechanical models of human ventricle based on CiPA framework

Heikhmakhtiar, AK, Qauli, AI, Fu’adah, YN, Pramudito, MA, Pramawijaya, IN, Marcellinus, A, Kim, YS, Vanheusden, FJ ORCID: https://orcid.org/0000-0003-2369-6189 and Lim, KM, 2025. Drug induced TdP risks classification assay using electro-mechanical models of human ventricle based on CiPA framework. Toxicological Research. ISSN 2234-2753 (Forthcoming)

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Abstract

Predicting drug-induced cardiac toxicity is critical for drug safety assays, especially when evaluating risks of inducing Torsade de Pointes (TdP). This study proposes an integrated electromechanical model of myocytes for TdP risk assessment, extending the CiPA framework. Human electrophysiological models of CiPAORdv1.0, ORD, and ToR were integrated with a Land mechanical ventricle model. Twenty-seven parameters were observed, including the net current (qNet), inward current (qInward), action potential profile, intracellular calcium profile, and tension profile. We used ordinal logistic regression with 12 drugs as training dataset and validated using unseen data from the remaining 16 drugs following the protocol from the FDA.

We observed that the electromechanical model improved the TdP risk classification in most of the parameters derived from the action potential, Calcium Transient (Cai), and tension profile. The CiPAORdv1.0+Land not only preserved qNet performance but improved the classification performance using APD50, APD90, CaD90, Catri, titri, and EMW. On the other hand, ToR+Land model improved the parameter of CaTD50 and CaD50,tp. The ORD+Land model showed some improvement in Vmax, and some well for other parameters including CaT90, CaD90,tp, Catri and titri.

This work highlights the advantage of an electromechanical ventricular model for assessing drug-induced TdP risk compared to an electrophysiological model. Overall, the coupled models improved the TdP classification performance based on APD and calcium as well as tension profile. Further optimization of the models and inclusion of more drugs in training the models can improve interpretability and predictive accuracy for TdP risk assessment.

Item Type:	Journal article
Publication Title:	Toxicological Research
Creators:	Heikhmakhtiar, A.K., Qauli, A.I., Fu’adah, Y.N., Pramudito, M.A., Pramawijaya, I.N., Marcellinus, A., Kim, Y.S., Vanheusden, F.J. and Lim, K.M.
Publisher:	Springer
Date:	10 September 2025
ISSN:	2234-2753
Identifiers:	Number Type 2515671 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	17 Oct 2025 07:47
Last Modified:	17 Oct 2025 07:47
URI:	https://irep.ntu.ac.uk/id/eprint/54590

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