Literature DB >> 29547274

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk.

Lucia Romero1, Jordi Cano1, Julio Gomis-Tena1, Beatriz Trenor1, Ferran Sanz2, Manuel Pastor2, Javier Saiz1.   

Abstract

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( IKs and IKr, respectively) and the L-type calcium current ( ICaL) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC50 based test.

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Year:  2018        PMID: 29547274     DOI: 10.1021/acs.jcim.7b00440

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  9 in total

1.  When Does the IC50 Accurately Assess the Blocking Potency of a Drug?

Authors:  Julio Gomis-Tena; Brandon M Brown; Jordi Cano; Beatriz Trenor; Pei-Chi Yang; Javier Saiz; Colleen E Clancy; Lucia Romero
Journal:  J Chem Inf Model       Date:  2020-03-10       Impact factor: 4.956

2.  Plasticizer Interaction With the Heart: Chemicals Used in Plastic Medical Devices Can Interfere With Cardiac Electrophysiology.

Authors:  Rafael Jaimes; Damon McCullough; Bryan Siegel; Luther Swift; Daniel McInerney; James Hiebert; Erick A Perez-Alday; Beatriz Trenor; Jiansong Sheng; Javier Saiz; Larisa G Tereshchenko; Nikki Gillum Posnack
Journal:  Circ Arrhythm Electrophysiol       Date:  2019-06-28

Review 3.  Risk Assessment of Drug-Induced Long QT Syndrome for Some COVID-19 Repurposed Drugs.

Authors:  Veronique Michaud; Pamela Dow; Sweilem B Al Rihani; Malavika Deodhar; Meghan Arwood; Brian Cicali; Jacques Turgeon
Journal:  Clin Transl Sci       Date:  2020-11-18       Impact factor: 4.689

Review 4.  Ventricular voltage-gated ion channels: Detection, characteristics, mechanisms, and drug safety evaluation.

Authors:  Lulan Chen; Yue He; Xiangdong Wang; Junbo Ge; Hua Li
Journal:  Clin Transl Med       Date:  2021-10

5.  All-Optical Electrophysiology Refines Populations of In Silico Human iPSC-CMs for Drug Evaluation.

Authors:  Michelangelo Paci; Elisa Passini; Aleksandra Klimas; Stefano Severi; Jari Hyttinen; Blanca Rodriguez; Emilia Entcheva
Journal:  Biophys J       Date:  2020-04-04       Impact factor: 4.033

Review 6.  In silico models for evaluating proarrhythmic risk of drugs.

Authors:  Minki Hwang; Chul-Hyun Lim; Chae Hun Leem; Eun Bo Shim
Journal:  APL Bioeng       Date:  2020-06-04

7.  In silico study of the effects of anti-arrhythmic drug treatment on sinoatrial node function for patients with atrial fibrillation.

Authors:  Jieyun Bai; Yaosheng Lu; Henggui Zhang
Journal:  Sci Rep       Date:  2020-01-15       Impact factor: 4.379

8.  Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk.

Authors:  Mark R Davies; Michael Martinec; Robert Walls; Roman Schwarz; Gary R Mirams; Ken Wang; Guido Steiner; Andy Surinach; Carlos Flores; Thierry Lavé; Thomas Singer; Liudmila Polonchuk
Journal:  Cell Rep Med       Date:  2020-08-25

9.  Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs.

Authors:  Yedam Yoo; Aroli Marcellinus; Da Un Jeong; Ki-Suk Kim; Ki Moo Lim
Journal:  Front Physiol       Date:  2021-12-10       Impact factor: 4.566

  9 in total

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