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Updated: Jun 13, 2026
Laser-Induced Action Potential-Like Measurements of Cardiomyocytes on Microelectrode Arrays for Increased Predictivity of Safety Pharmacology
Published on: September 13, 2022
Systems pharmacology of arrhythmias
Seth I Berger1, Avi Ma'ayan, Ravi Iyengar
1Department of Pharmacology and Systems Therapeutics and Systems Biology Center New York, Mount Sinai School of Medicine, One Gustave L. Levy Place, Box 1215, New York, NY 10029, USA. Ravi.Iyengar@mssm.edu
Abstract:
Long QT syndrome (LQTS) is a congenital or drug-induced change in electrical activity of the heart that can lead to fatal arrhythmias. Mutations in 12 genes encoding ion channels and associated proteins are linked with congenital LQTS. With a computational systems biology approach, we found that gene products involved in LQTS formed a distinct functional neighborhood within the human interactome. Other diseases form similarly selective neighborhoods, and comparison of the LQTS neighborhood with other disease-centered neighborhoods suggested a molecular basis for associations between seemingly unrelated diseases that have increased risk of cardiac complications. By combining the LQTS neighborhood with published genome-wide association study data, we identified previously unknown single-nucleotide polymorphisms likely to affect the QT interval. We found that targets of U.S. Food and Drug Administration (FDA)-approved drugs that cause LQTS as an adverse event were enriched in the LQTS neighborhood. With the LQTS neighborhood as a classifier, we predicted drugs likely to have risks for QT effects and we validated these predictions with the FDA's Adverse Events Reporting System, illustrating how network analysis can enhance the detection of adverse drug effects associated with drugs in clinical use. Thus, the identification of disease-selective neighborhoods within the human interactome can be useful for predicting new gene variants involved in disease, explaining the complexity underlying adverse drug side effects, and predicting adverse event susceptibility for new drugs.
Insights
This study reveals how Long QT syndrome (LQTS) gene networks connect diseases and predict drug risks. Network analysis aids in identifying genetic variants and adverse drug effects for better cardiac safety.
Area of Science:
- Genomics
- Systems Biology
- Pharmacology
Background:
- Long QT syndrome (LQTS) involves heart electrical changes, often due to genetic mutations in ion channel-related genes.
- LQTS can lead to fatal arrhythmias and is sometimes induced by medications.
Purpose of the Study:
- To investigate the functional network of LQTS-associated genes within the human interactome.
- To explore potential molecular links between LQTS and other diseases with cardiac risks.
- To identify novel genetic variants and predict adverse drug effects related to QT interval prolongation.
Main Methods:
- Utilized a computational systems biology approach to analyze the LQTS gene network.
- Integrated LQTS network data with genome-wide association study (GWAS) findings.
- Employed network analysis to predict and validate drug-induced QT effects using FDA data.
Main Results:
- LQTS-related gene products form a distinct functional neighborhood in the human interactome.
- Disease neighborhoods reveal molecular underpinnings for associations between unrelated diseases with cardiac risks.
- Identified novel single-nucleotide polymorphisms (SNPs) affecting the QT interval and confirmed enrichment of FDA-approved drug targets in the LQTS network.
- Successfully predicted and validated drugs with potential QT effects, demonstrating network analysis utility.
Conclusions:
- Disease-selective gene networks in the human interactome offer insights into disease associations and genetic risk factors.
- Network analysis is a powerful tool for predicting adverse drug reactions, particularly QT prolongation.
- This approach enhances the detection of drug side effects and aids in predicting susceptibility to adverse events for new medications.
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