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

Introduction Unwanted drug interactions with ionic currents in the heart can lead to an increased proarrhythmic 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. Methods We quantified the variability associated with concentration-effect curves fitted to recordings from high-throughput Molecular Devices IonWorks® Quattro™ screens when detecting block of IKr (hERG), INa (NaV1.5), ICaL (CaV1.2), IKs (KCNQ1/minK) and Ito (Kv4.3/KChIP2.2), and the Molecular Devices FLIPR® Tetra fluorescence screen for ICaL (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. Results 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. Discussion 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.