Work

Toward Digital Twin Technology for Precision Pharmacology

Year: 2023

Type: article

Abstract: The authors demonstrate the feasibility of technological innovation for personalized medicine in the context of drug-induced arrhythmia. The authors use atomistic-scale structural models to predict rates of drug interaction with ion channels and make predictions of their effects in digital twins of induced pluripotent stem cell-derived cardiac myocytes. The authors construct a simplified multilayer, 1-dimensional ring model with sufficient path length to enable the prediction of arrhythmogenic dispersion of repolarization. Finally, the authors validate the computational pipeline prediction of drug effects with data and quantify drug-induced propensity to repolarization abnormalities in cardiac tissue. The technology is high throughput, computationally efficient, and low cost toward personalized pharmacologic prediction.

Source: JACC. Clinical electrophysiology

DOI: 10.1016/j.jacep.2023.10.024

Authors: Pei-Chi Yang, Mao-Tsuen Jeng, Vladimir Yarov-Yarovoy, L. Fernando Santana, Igor Vorobyov, Colleen E. Clancy

Institution: University of California, Davis

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: hybrid

Estimated APC paid: $4,500