Sophion analyzer as a tool for biophysical and pharmacological characterization of eight Nav subtypes evaluated in parallel on Qube


Neuroscience 2016


Mads P. G. Korsgaard, Lars Damgaard Løjkner, Anders Lindqvist, Mette T Christensen, Thomas Binzer



Drug discovery on ion channels is a slow and complicated process and demands a high throughput system with high data quality, but also with a flexible design and easy-to-analyze data. Sophion Qube is a next-generation giga-seal automated patch clamp (APC) screening instrument, capable of testing thousands of compounds with a single click on a button. Data analysis is as important as data acquisition. In HTS the need for powerful analysis with efficient quality filtering is evident in order to handle the vast amount of data generated on an electrophysiological platform.

Qube is capable of testing up to 16 different cell lines or cell clones in one experiment. This can be utilized to test a different panel of cell lines or for selecting the best suited cell clone before embarking on a HTS campaign. The integrated analysis software, Sophion Analyzer, ensures analysis to keep track of all the results and whenever another QChip is assayed the analysis is done with the same set of user defined criteria.
Here we demonstrate the power of automated analysis by exploring three types of experiments executed on eight different Nav channel subtypes; 1) TTX sensitivity, 2) IV-relationship for activation and inactivation and 3) pulse train suitable for screening for use dependent sodium channels blockers. For every run Nav1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7 and 1.8 were tested in parallel on a QChip. Recorded ion channel whole-cell currents were automatically analyzed for IV-relationships for activation and inactivation (V½ and Boltzmann fit and time constants) and concentration-dependent drug effects (Hill fit and IC50) were analyzed using the Sophion Analyzer. If preferred, data can by a click be exported and calculated by other programs as Spotfire, Genedata screener or implemented in in-house software.
For each subtype, the experiments identified the expected pharmacology for use- and state-dependent drugs as well as biophysical properties. The findings determined the differences between the different subtypes as expected and also that post experiment analysis can be performed with minimum of effort when using integrated, automated analysis software.

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