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

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/dtMax_repol) was the best when merging all in silico features computed through three in vitro datasets.

Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR–ORd in silico model output

“…Higher drug doses correspond to faster and larger increments in qInward values ( Li et al, 2017 ). Consistent with our findings, prior research has also highlighted the dominant role of qInward variability in convolutional neural network (CNN) classifiers for TdP risk classification ( Jeong et al, 2022 ).…”

“…A previous study utilized the ToR–ORd model to evaluate the TdP metrics derived from single APs, intracellular calcium dynamics, and ionic charge obtained from the effects of drugs on the ToR–ORd ventricular cell model to classify TdP risks using ordinal logistic regression ( Jeong et al, 2022 ). However, using single TdP metrics from the in silico models for classifying the TdP risks is challenging to handle complex or non-linear relationships between independent and dependent variables.…”

“…Several studies have applied various TdP metrics from the ORd or CiPAORdv1.0 ventricular cell model to classify TdP risks ( Mirams et al, 2011 ; Lancaster and Sobie, 2016 ; Passini et al, 2017 ; Li et al, 2019 ; Yoo et al, 2021 ; Jeong et al, 2022 ). In their study, Mirams et al (2011) utilized the ventricular cell model developed by Grandi et al (2010) .…”

qInward variability-based in-silico proarrhythmic risk assessment of drugs using deep learning model

“…Han et al suggested the selection method of calibration drugs and calibrated the previous OLR model by validating it using two lab-specific datasets ( Han et al, 2020 ). In a previous study, we validated 12 promising in silico features using OLR, based on in-vitro datasets used in the AP simulation ( Jeong et al, 2022a ). In this study, we determined the CNN classifier using the variability of in silico features calculated using the Chantest dataset and validated the corresponding robustness using three merged in-vitro datasets.…”

“…In our previous study, we validated the robustness of 12 in silico features using an ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the used in-vitro experimental datasets ( Jeong et al, 2022a ). However, as the results of the OLR model using 12 in silico features were desirable, the single value of the in silico feature was perceived as limiting to classifying the three TdP risks.…”