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Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System.
Lei, Chon Lok; Clerx, Michael; Gavaghan, David J; Polonchuk, Liudmila; Mirams, Gary R; Wang, Ken.
Afiliación
  • Lei CL; Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
  • Clerx M; Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
  • Gavaghan DJ; Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
  • Polonchuk L; Pharma Research and Early Development, Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
  • Mirams GR; Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom. Electronic address: gary.mirams@nottingham.ac.uk.
  • Wang K; Pharma Research and Early Development, Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
Biophys J ; 117(12): 2438-2454, 2019 12 17.
Article en En | MEDLINE | ID: mdl-31447109
ABSTRACT
Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug development and requires a deep understanding of a compound's action on ion channels. In vitro hERG channel current recordings are an important step in evaluating the proarrhythmic potential of small molecules and are now routinely performed using automated high-throughput patch-clamp platforms. These machines can execute traditional voltage-clamp protocols aimed at specific gating processes, but the array of protocols needed to fully characterize a current is typically too long to be applied in a single cell. Shorter high-information protocols have recently been introduced that have this capability, but they are not typically compatible with high-throughput platforms. We present a new 15 second protocol to characterize hERG (Kv11.1) kinetics, suitable for both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, by applying it to Chinese hamster ovary cells stably expressing hERG1a. From these recordings, we construct 124 cell-specific variants/parameterizations of a hERG model at 25°C. A further eight independent protocols are run in each cell and are used to validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we use to quantify the uncertainty in the model parameters, and their variability from cell-to-cell; we use this model to suggest reasons for the variability. This study demonstrates a robust method to measure and quantify uncertainty and shows that it is possible and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Canales de Potasio Éter-A-Go-Go Tipo de estudio: Guideline / Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Biophys J Año: 2019 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Canales de Potasio Éter-A-Go-Go Tipo de estudio: Guideline / Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Biophys J Año: 2019 Tipo del documento: Article País de afiliación: Reino Unido