Suscripción a Biblioteca: Guest
Critical Reviews™ in Biomedical Engineering

Publicado 6 números por año

ISSN Imprimir: 0278-940X

ISSN En Línea: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering

Volumen 46, Edición 3, 2018, pp. 245-275
DOI: 10.1615/CritRevBiomedEng.2018026492
Get accessGet access

SINOPSIS

A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q–T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de pointes). Such prolongation is also a widely accepted cardiac safety biomarker in drug development. Current preclinical drug-safety assays include patch clamp analysis to evaluate drug-related blockade of cardiac repolarizing ion currents. Recently reported patch clamp assay results have shown predictive sensitivities and specificities in the ranges of 64%–82% and 75%–88%, respectively. In this project, we use a support vector machine classifier to find mean sensitivities and specificities of 85% and 90%, respectively, across 77 drug subclassifications. Clustering by gene expression profile similarities shows that drugs known to prolong the QT interval do not always form distinct groups, but the number of groups is limited. The most common biological network links associated with these groups involve genes linked with fatty acid metabolism, G proteins, intracellular glutathione, immune responses, apoptosis, mitochondrial function, electron transport, and mitogen-activated protein kinases. These results suggest that machine-learning analysis of gene expression and clustering may augment cardiac safety predictions for improving drug-safety assessments.

CITADO POR
  1. Chung Cheuk To, Bazoukis George, Lee Sharen, Liu Ying, Liu Tong, Letsas Konstantinos P., Armoundas Antonis A., Tse Gary, Machine learning techniques for arrhythmic risk stratification: a review of the literature, International Journal of Arrhythmia, 23, 1, 2022. Crossref

  2. Mak Kit-Kay, Balijepalli Madhu Katyayani, Pichika Mallikarjuna Rao, Success stories of AI in drug discovery - where do things stand?, Expert Opinion on Drug Discovery, 17, 1, 2022. Crossref

Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones Precios y Políticas de Suscripcione Begell House Contáctenos Language English 中文 Русский Português German French Spain