Work

A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers

Year: 2024

Type: article

Abstract: This study addresses the critical issue of drug-induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. Existing methodologies, particularly those reliant on a single biomarker derived from CiPA O'Hara-Rudy (CiPAORdv1.0) ventricular cell model without the hERG dynamic as input to the individual machine learning model, have limitations in capturing the complexity inherent in the comprehensive range of factors influencing drug-induced TdP risk. This study aims to overcome these limitations by proposing a stacking ensemble machine learning approach by integrating multiple in silico biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. The ensemble machine learning model consisted of three artificial neural network (ANN) models as baseline model and support vector machine (SVM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models as meta-classifier. The highest AUC score of 1.00 (0.90-1.00) for high risk, 0.97 (0.84-1.00) for intermediate risk, and 1.00 (0.87-1.00) for low risk were obtained using seven biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. Furthering our investigation, we explored the model's robustness by incorporating interindividual variability into the generation of in silico biomarkers from a population of human ventricular cell models. This study also enabled an analysis of TdP risk classification under high clinical exposure and therapeutic scenarios for several drugs. Additionally, from a sensitivity analysis, we revealed four important ion channels, namely, CaL, NaL, Na, and Kr channels that affect significantly the important biomarkers for TdP risk prediction.

Source: CPT Pharmacometrics & Systems Pharmacology

DOI: 10.1002/psp4.13229

Authors: Yunendah Nur Fuadah, Ali Ikhsanul Qauli, Muhammad Adnan Pramudito, Aroli Marcellinus, Ulfa Latifa Hanum, Ki Moo Lim

Institutions: Kumoh National Institute of Technology, Telkom University, Universitas Airlangga

Language: English

Topic: Cardiac electrophysiology and arrhythmias

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,940