PT - JOURNAL ARTICLE AU - Monique J. Windley AU - Stefan A. Mann AU - Jamie I Vandenberg AU - Adam Hill TI - Temperature effects on kinetics of Kv11.1 drug block have important consequences for in silico proarrhythmic risk prediction. AID - 10.1124/mol.115.103127 DP - 2016 Jan 01 TA - Molecular Pharmacology PG - mol.115.103127 4099 - http://molpharm.aspetjournals.org/content/early/2016/05/12/mol.115.103127.short 4100 - http://molpharm.aspetjournals.org/content/early/2016/05/12/mol.115.103127.full AB - Drug block of KV11.1 (hERG) channels, encoded by the KCNH2 gene, is associated with reduced repolarization of the cardiac action potential and is the predominant cause of acquired long QT syndrome that can lead to fatal cardiac arrhythmias. Current safety guidelines require that potency of KV11.1 block is assessed in the preclinical phase of drug development. However, not all drugs that block KV11.1 are proarrhythmic, meaning that screening based on equilibrium measures of block can result in high attrition of potentially low risk drugs. The next generation of drug screening approaches are set to be based around in silico risk prediction, informed by in vitro mechanistic descriptions of drug binding, including measures of the kinetics of block. A critical issue in this regard is characterizing the temperature dependence of drug binding. Specifically, it is important to address whether kinetics relevant to physiological temperatures can be inferred or extrapolated from in vitro data gathered at room temperature in high throughout systems. Here we present the first complete study of the temperature dependent kinetics of block and unblock of a proarrhythmic drug, cisapride, to Kv11.1. Our data highlight a complexity to binding that manifests at higher temperatures that can be explained by accumulation of an intermediate, non-productively bound, encounter complex. These results suggest that for cisapride, physiologically relevant kinetic parameters cannot be simply extrapolated from those measured at lower temperatures, but rather data gathered at physiological temperatures should be used to constrain in silico models that may be used for proarrhythmic risk prediction.