Literature DB >> 30770456

Protocol-Dependent Differences in IC50 Values Measured in Human Ether-Á-Go-Go-Related Gene Assays Occur in a Predictable Way and Can Be Used to Quantify State Preference of Drug Binding.

William Lee1, Monique J Windley1, Matthew D Perry1, Jamie I Vandenberg1, Adam P Hill2.   

Abstract

Current guidelines around preclinical screening for drug-induced arrhythmias require the measurement of the potency of block of voltage-gated potassium channel subtype 11.1 (Kv11.1) as a surrogate for risk. A shortcoming of this approach is that the measured IC50 of Kv11.1 block varies widely depending on the voltage protocol used in electrophysiological assays. In this study, we aimed to investigate the factors that contribute to these differences and to identify whether it is possible to make predictions about protocol-dependent block that might facilitate the comparison of potencies measured using different assays. Our data demonstrate that state preferential binding, together with drug-binding kinetics and trapping, is an important determinant of the protocol dependence of Kv11.1 block. We show for the first time that differences in IC50 measured between protocols occurs in a predictable way, such that machine-learning algorithms trained using a selection of simple voltage protocols can indeed predict protocol-dependent potency. Furthermore, we also show that the preference of a drug for binding to the open versus the inactivated state of Kv11.1 can also be inferred from differences in IC50 values measured between protocols. Our work therefore identifies how state preferential drug binding is a major determinant of the protocol dependence of IC50 values measured in preclinical Kv11.1 assays. It also provides a novel method for quantifying the state dependence of Kv11.1 drug binding that will facilitate the development of more complete models of drug binding to Kv11.1 and improve our understanding of proarrhythmic risk associated with compounds that block Kv11.1.
Copyright © 2019 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2019        PMID: 30770456     DOI: 10.1124/mol.118.115220

Source DB:  PubMed          Journal:  Mol Pharmacol        ISSN: 0026-895X            Impact factor:   4.436


  7 in total

1.  Design of New Potent and Selective Thiophene-Based KV1.3 Inhibitors and Their Potential for Anticancer Activity.

Authors:  Špela Gubič; Louise Antonia Hendrickx; Xiaoyi Shi; Žan Toplak; Štefan Možina; Kenny M Van Theemsche; Ernesto Lopes Pinheiro-Junior; Steve Peigneur; Alain J Labro; Luis A Pardo; Jan Tytgat; Tihomir Tomašič; Lucija Peterlin Mašič
Journal:  Cancers (Basel)       Date:  2022-05-24       Impact factor: 6.575

2.  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

3.  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

4.  Rapid Characterization of hERG Channel Kinetics II: Temperature Dependence.

Authors:  Chon Lok Lei; Michael Clerx; Kylie A Beattie; Dario Melgari; Jules C Hancox; David J Gavaghan; Liudmila Polonchuk; Ken Wang; Gary R Mirams
Journal:  Biophys J       Date:  2019-07-25       Impact factor: 4.033

5.  Cardiac TdP risk stratification modelling of anti-infective compounds including chloroquine and hydroxychloroquine.

Authors:  Dominic G Whittaker; Rebecca A Capel; Maurice Hendrix; Xin Hui S Chan; Neil Herring; Nicholas J White; Gary R Mirams; Rebecca-Ann B Burton
Journal:  R Soc Open Sci       Date:  2021-04-13       Impact factor: 2.963

6.  A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm.

Authors:  Bradley J Ridder; Derek J Leishman; Matthew Bridgland-Taylor; Mohammadreza Samieegohar; Xiaomei Han; Wendy W Wu; Aaron Randolph; Phu Tran; Jiansong Sheng; Timm Danker; Anders Lindqvist; Daniel Konrad; Simon Hebeisen; Liudmila Polonchuk; Evgenia Gissinger; Muthukrishnan Renganathan; Bryan Koci; Haiyang Wei; Jingsong Fan; Paul Levesque; Jae Kwagh; John Imredy; Jin Zhai; Marc Rogers; Edward Humphries; Robert Kirby; Sonja Stoelzle-Feix; Nina Brinkwirth; Maria Giustina Rotordam; Nadine Becker; Søren Friis; Markus Rapedius; Tom A Goetze; Tim Strassmaier; George Okeyo; James Kramer; Yuri Kuryshev; Caiyun Wu; Herbert Himmel; Gary R Mirams; David G Strauss; Rémi Bardenet; Zhihua Li
Journal:  Toxicol Appl Pharmacol       Date:  2020-03-21       Impact factor: 4.219

7.  Four Ways to Fit an Ion Channel Model.

Authors:  Michael Clerx; Kylie A Beattie; David J Gavaghan; Gary R Mirams
Journal:  Biophys J       Date:  2019-08-06       Impact factor: 4.033

  7 in total

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