3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance

Scientific Reports
Santiago VilarGeorge Hripcsak

Abstract

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to t...Continue Reading

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Citations

Dec 8, 2015·Computers in Biology and Medicine·Hossein RahmaniAndreas Bender
Nov 16, 2016·Scientific Reports·Santiago VilarGeorge Hripcsak
Nov 6, 2018·Journal of the American Medical Informatics Association : JAMIA·George HripcsakPatrick B Ryan
Mar 17, 2016·Nanomedicine·Jose F RodriguesOsvaldo N de Oliveira
Dec 19, 2019·BMC Medical Informatics and Decision Making·Ruoqi Liu, Ping Zhang
Dec 20, 2018·Journal of Clinical Pharmacy and Therapeutics·Dalong SongJun Liao
Dec 7, 2018·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Baofang HuWeihua Yuan
Oct 17, 2019·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Baofang HuZhenmei Yu

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Software Mentioned

AUROC
Schrödinger package
R
Dragon
SIDER
LigPrep
MCMM
Schrödinger
Macromodel
SPSS Statistics

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