Validation of in silico biomarkers for drug screening through ordinal logistic regression

Da Un Jeong; Rakha Zharfarizqi Danadibrata; Aroli Marcellinus; Ki Moo Lim

doi:10.3389/fphys.2022.1009647

Validation of in silico biomarkers for drug screening through ordinal logistic regression

Front Physiol. 2022 Oct 6:13:1009647. doi: 10.3389/fphys.2022.1009647. eCollection 2022.

Authors

Da Un Jeong¹, Rakha Zharfarizqi Danadibrata¹, Aroli Marcellinus¹, Ki Moo Lim^{1

2}

Affiliations

¹ Computational Medicine Lab, Kumoh National Institute of Technology, Department of IT Convergence Engineering, Gumi, South Korea.
² Computational Medicine Lab, Kumoh National Institute of Technology, Department of Medical IT Convergence Engineering, Gumi, South Korea.

Abstract

Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiological simulations using in vitro experimental datasets as input, therefore changing with the quality of in vitro experimental data; however, research to validate the robustness of in silico features for proarrhythmic risk assessment of drugs depending on in vitro datasets has not been conducted. This study aims to verify the availability of in silico features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three in vitro datasets measured under different experimental environments and with different purposes. We performed in silico drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12 in silico features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each in vitro data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12 in silico features and used in vitro datasets to validate which in silico feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12 in silico features helped determine high-risky torsadogenic drugs, regardless of the in vitro datasets used in the in silico simulation as input. In the three types of in silico features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD₅₀) was the best when individually using in silico features per in vitro dataset. In contrast, the AP repolarization velocity (dVm/dt_{Max_repol}) was the best when merging all in silico features computed through three in vitro datasets.

Keywords: drug toxicity; in silico biomarkers; ordinal logistic regression; proarrhythmic risk; torsades de pointes.