Referenced in: none

Automatically associated channels: Cav1.2 , KChIP2 , Kv1.4 , Kv11.1 , Kv3.1 , Kv4.3 , Kv7.1 , Nav1.5

Title: Variability in high-throughput ion-channel screening data and consequences for cardiac safety assessment.

Authors: Ryan C Elkins, Mark R Davies, Stephen J Brough, David J Gavaghan, Yi Cui, Najah Abi-Gerges, Gary R Mirams

Journal, date & volume: J Pharmacol Toxicol Methods, 2013 Jul-Aug , 68, 112-22

PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/23651875

Abstract
Unwanted drug interactions with ionic currents in the heart can lead to an increased pro-arrhythmic risk to patients in the clinic. It is therefore a priority for safety pharmacology teams to detect block of cardiac ion channels, and new technologies have enabled the development of automated and high-throughput screening assays using cell lines. As a result of screening multiple ion-channels there is a need to integrate information, particularly for compounds affecting more than one current, and mathematical electrophysiology in-silico action potential models are beginning to be used for this.We quantified the variability associated with concentration-effect curves fitted to recordings from high-throughput Molecular Devices IonWorks® Quattro™ screens when detecting block of I(Kr) (hERG), I(Na) (NaV1.5), I(CaL) (CaV1.2), I(Ks) (KCNQ1/minK) and I(to) (Kv4.3/KChIP2.2), and the Molecular Devices FLIPR® Tetra fluorescence screen for I(CaL) (CaV1.2), for control compounds used at AstraZeneca and GlaxoSmithKline. We examined how screening variability propagates through in-silico action potential models for whole cell electrical behaviour, and how confidence intervals on model predictions can be estimated with repeated simulations.There are significant levels of variability associated with high-throughput ion channel electrophysiology screens. This variability is of a similar magnitude for different cardiac ion currents and different compounds. Uncertainty in the Hill coefficients of reported concentration-effect curves is particularly high. Depending on a compound's ion channel blocking profile, the uncertainty introduced into whole-cell predictions can become significant.Our technique allows confidence intervals to be placed on computational model predictions that are based on high-throughput ion channel screens. This allows us to suggest when repeated screens should be performed to reduce uncertainty in a compound's action to acceptable levels, to allow a meaningful interpretation of the data.