期刊
AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY
卷 304, 期 1, 页码 H104-H117出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/ajpheart.00511.2012
关键词
computational modeling; human ether-a-go-go-related gene ion channel; drug-binding kinetics; QT prolongation; Torsades de Pointes arrhythmia
资金
- British Heart Foundation [RG/11/18/29257] Funding Source: researchfish
- British Heart Foundation [RG/11/18/29257] Funding Source: Medline
Di Veroli GY, Davies MR, Zhang H, Abi-Gerges N, Boyett MR. High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment. Am J Physiol Heart Circ Physiol 304: H104-H117, 2013. First published October 26, 2012; doi:10.1152/ajpheart.00511.2012.-The use of computational models to predict drug-induced changes in the action potential (AP) is a promising approach to reduce drug safety attrition but requires a better representation of more complex drug-target interactions to improve the quantitative prediction. The blockade of the human ether-a-go-go-related gene (HERG) channel is a major concern for QT prolongation and Torsade de Pointes risk. We aim to develop quantitative in-silico AP predictions based on a new electrophysiological protocol (suitable for high-throughput HERG screening) and mathematical modeling of ionic currents. Electrophysiological recordings using the IonWorks device were made from HERG channels stably expressed in Chinese hamster ovary cells. A new protocol that delineates inhibition over time was applied to assess dofetilide, cisapride, and almokalant effects. Dynamic effects displayed distinct profiles for these drugs compared with concentration-effects curves. Binding kinetics to specific states were identified using a new HERG Markov model. The model was then modified to represent the canine rapid delayed rectifier K+ current at 37 degrees C and carry out AP predictions. Predictions were compared with a simpler model based on conductance reduction and were found to be much closer to experimental data. Improved sensitivity to concentration and pacing frequency variables was obtained when including binding kinetics. Our new electrophysiological protocol is suitable for high-throughput screening and is able to distinguish drug-binding kinetics. The association of this protocol with our modeling approach indicates that quantitative predictions of AP modulation can be obtained, which is a significant improvement compared with traditional conductance reduction methods.
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