title: High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment.
abstract: 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°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.
authors.affiliation: Institute of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom.
authors.forename: Giovanni Y
authors.initials: GY
authors.lastname: Di Veroli
authors.forename: Mark R
authors.initials: MR
authors.lastname: Davies
authors.forename: Henggui
authors.initials: H
authors.lastname: Zhang
authors.forename: Najah
authors.initials: N
authors.lastname: Abi-Gerges
authors.forename: Mark R
authors.initials: MR
authors.lastname: Boyett
mesh_terms: D000200:Action Potentials; D000818:Animals; D016466:CHO Cells; D020117:Cisapride / Q000633:toxicity; D003198:Computer Simulation; D006224:Cricetinae; D003412:Cricetulus; D004285:Dogs; D004305:Dose-Response Relationship, Drug; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors* / Q000235:genetics / Q000378:metabolism; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D007700:Kinetics; D008133:Long QT Syndrome / Q000139:chemically induced / Q000378:metabolism; D008390:Markov Chains; D008955:Models, Cardiovascular; D018408:Patch-Clamp Techniques; D010627:Phenethylamines / Q000633:toxicity; D026902:Potassium Channel Blockers / Q000378:metabolism / Q000633:toxicity*; D011412:Propanolamines / Q000633:toxicity; D011485:Protein Binding; D018570:Risk Assessment; D013449:Sulfonamides / Q000633:toxicity; D016171:Torsades de Pointes / Q000139:chemically induced / Q000378:metabolism; D018675:Toxicity Tests; D014162:Transfection
chemical_list: D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; C000606913:KCNH2 protein, human; D010627:Phenethylamines; D026902:Potassium Channel Blockers; D011412:Propanolamines; D013449:Sulfonamides; C074334:almokalant; C063533:dofetilide; D020117:Cisapride