Literature DB >> 27717849

Am I or am I not proarrhythmic? Comparison of various classifications of drug TdP propensity.

Barbara Wiśniowska1, Sebastian Polak2.   

Abstract

This review aims to present and compare various Torsade de pointes propensity classification schemes of drugs that are publicly available from many scientific sources. We have also tracked and listed the compounds that were differently categorized. Additionally, we would like to draw attention to the need for establishing a general, standardized classification of a drug's proarrhythmic propensity. This is especially important in the current drug development process because of the changing paradigm of drug cardiac safety assessment.
Copyright © 2016 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27717849     DOI: 10.1016/j.drudis.2016.09.027

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  9 in total

1.  Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram.

Authors:  Nikunjkumar Patel; Barbara Wiśniowska; Masoud Jamei; Sebastian Polak
Journal:  AAPS J       Date:  2017-11-27       Impact factor: 4.009

2.  Drug-Induced Arrhythmia: Bridging the Gap Between Pathophysiological Knowledge and Clinical Practice.

Authors:  Elisabetta Poluzzi; Emanuel Raschi; Igor Diemberger; Fabrizio De Ponti
Journal:  Drug Saf       Date:  2017-06       Impact factor: 5.606

3.  Complex versus simple models: ion-channel cardiac toxicity prediction.

Authors:  Hitesh B Mistry
Journal:  PeerJ       Date:  2018-02-05       Impact factor: 2.984

4.  Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development.

Authors:  Sebastian Polak; Klaus Romero; Alexander Berg; Nikunjkumar Patel; Masoud Jamei; David Hermann; Debra Hanna
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-03-08       Impact factor: 2.745

5.  Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features.

Authors:  Jaimit Parikh; Viatcheslav Gurev; John J Rice
Journal:  Front Pharmacol       Date:  2017-11-14       Impact factor: 5.810

6.  Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning.

Authors:  Francisco Sahli-Costabal; Kinya Seo; Euan Ashley; Ellen Kuhl
Journal:  Biophys J       Date:  2020-01-22       Impact factor: 4.033

7.  General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy.

Authors:  Zhihua Li; Gary R Mirams; Takashi Yoshinaga; Bradley J Ridder; Xiaomei Han; Janell E Chen; Norman L Stockbridge; Todd A Wisialowski; Bruce Damiano; Stefano Severi; Pierre Morissette; Peter R Kowey; Mark Holbrook; Godfrey Smith; Randall L Rasmusson; Michael Liu; Zhen Song; Zhilin Qu; Derek J Leishman; Jill Steidl-Nichols; Blanca Rodriguez; Alfonso Bueno-Orovio; Xin Zhou; Elisa Passini; Andrew G Edwards; Stefano Morotti; Haibo Ni; Eleonora Grandi; Colleen E Clancy; Jamie Vandenberg; Adam Hill; Mikiko Nakamura; Thomas Singer; Liudmila Polonchuk; Andrea Greiter-Wilke; Ken Wang; Stephane Nave; Aaron Fullerton; Eric A Sobie; Michelangelo Paci; Flora Musuamba Tshinanu; David G Strauss
Journal:  Clin Pharmacol Ther       Date:  2019-11-10       Impact factor: 6.903

8.  Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator.

Authors:  Beth McMillan; David J Gavaghan; Gary R Mirams
Journal:  Toxicol Res (Camb)       Date:  2017-09-14       Impact factor: 3.524

9.  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 in total

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