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

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 cons

SUBMITTER: Fuadah YN 

PROVIDER: S-EPMC11646942 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature