Literature DB >> 19932726

Prediction of drug-related cardiac adverse effects in humans--A: creation of a database of effects and identification of factors affecting their occurrence.

Edwin J Matthews1, Anna A Frid.   

Abstract

This is the first of two reports that describes the compilation of a database of drug-related cardiac adverse effects (AEs) that was used to construct quantitative structure-activity relationship (QSAR) models to predict these AEs, to identify properties of pharmaceuticals correlated with the AEs, and to identify plausible mechanisms of action (MOAs) causing the AEs. This database of 396,985 cardiac AE reports was linked to 1632 approved drugs and their chemical structures, 1851 clinical indications (CIs), 997 therapeutic targets (TTs), 432 pharmacological MOAs, and 21,180 affinity coefficients (ACs) for the MOA receptors. AEs were obtained from the Food and Drug Administration's (FDA's) Spontaneous Reporting System (SRS) and Adverse Event Reporting System (AERS) and publicly available medical literature. Drug TTs were obtained from Integrity; drug MOAs and ACs were predicted by BioEpisteme. Significant cardiac AEs and patient exposures were estimated based on the proportional reporting ratios (PRRs) for each drug and each AE endpoint as a percentage of the total AEs. Cardiac AE endpoints were bundled based on toxicological mechanism and concordance of drug-related findings. Results revealed that significant cardiac AEs formed 9 clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes), and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). Based on the observation that each drug had one TT and up to 9 off-target MOAs, cardiac AEs were highly correlated with drugs affecting cardiovascular and cardioneurological functions and certain MOAs (e.g., alpha- and beta-adeno, dopamine, and hydroxytryptomine receptors). Copyright 2010. Published by Elsevier Inc.

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Year:  2009        PMID: 19932726     DOI: 10.1016/j.yrtph.2009.11.006

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  6 in total

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Authors:  Alexey V Zakharov; Alexey A Lagunin; Dmitry A Filimonov; Vladimir V Poroikov
Journal:  Chem Res Toxicol       Date:  2012-11-02       Impact factor: 3.739

2.  In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers.

Authors:  Chuipu Cai; Jiansong Fang; Pengfei Guo; Qi Wang; Huixiao Hong; Javid Moslehi; Feixiong Cheng
Journal:  J Chem Inf Model       Date:  2018-05-10       Impact factor: 4.956

3.  Large-scale prediction and testing of drug activity on side-effect targets.

Authors:  Eugen Lounkine; Michael J Keiser; Steven Whitebread; Dmitri Mikhailov; Jacques Hamon; Jeremy L Jenkins; Paul Lavan; Eckhard Weber; Allison K Doak; Serge Côté; Brian K Shoichet; Laszlo Urban
Journal:  Nature       Date:  2012-06-10       Impact factor: 49.962

4.  Relationships Between Pharmacovigilance, Molecular, Structural, and Pathway Data: Revealing Mechanisms for Immune-Mediated Drug-Induced Liver Injury.

Authors:  S S Ho; A J McLachlan; T F Chen; D E Hibbs; R A Fois
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-06-18

5.  Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions.

Authors:  Sergey Ivanov; Alexey Lagunin; Dmitry Filimonov; Vladimir Poroikov
Journal:  PLoS Comput Biol       Date:  2019-07-19       Impact factor: 4.475

6.  DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels.

Authors:  Cheng Yan; Guihua Duan; Yi Pan; Fang-Xiang Wu; Jianxin Wang
Journal:  BMC Bioinformatics       Date:  2019-12-24       Impact factor: 3.169

  6 in total

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