Slow delayed rectifying potassium current (IKs) – analysis of the in vitro inhibition data and predictive model development
Article first published online: 14 FEB 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Journal of Applied Toxicology
Volume 33, Issue 8, pages 723–739, August 2013
How to Cite
Polak, S., Wiśniowska, B., Glinka, A., Fijorek, K. and Mendyk, A. (2013), Slow delayed rectifying potassium current (IKs) – analysis of the in vitro inhibition data and predictive model development. J. Appl. Toxicol., 33: 723–739. doi: 10.1002/jat.2719
- Issue published online: 21 JUN 2013
- Article first published online: 14 FEB 2012
- Manuscript Revised: 21 DEC 2011
- Manuscript Accepted: 21 DEC 2011
- Manuscript Received: 23 NOV 2011
- potassium channels;
- slow rectifying potassium current;
The excitable cell membranes contain ion channels that allow the ions passage through the specific pores via a passive process. Assessment of the inhibition of the IKr (hERG) current is considered to be the main target during the drug development process, although there are other ionic currents for which drug-triggered modification can either potentiate or mask hERG channel blockade. Information describing the results of in vitro studies investigating the chemical–IKs current interactions has been developed in the current study. Based on the publicly available data sources, 145 records were collected. The final list of publications consists of 64 positions and refers to 106 different molecules connected with IKs current inhibition, with at least one IC50 value measured. Ultimately, 98 of the IC50 values expressed as absolute values were gathered. For 36 records the IC50 was expressed as a relative value. For the 11 remaining records, the inhibition was not clearly expressed. Based on the collected data the predictive models for the IC50 estimation were developed with the use of various algorithms. The extended Quantitative Structure-Activity Relationships (QSAR) methodology was applied and the in vitro research settings were included as independent variables, apart from the physico-chemical descriptors calculated with the use of the Marvin Calculator Plugins. The root mean squared error and normalized root mean squared error values for the best model (an expert system based on two independent artificial neural networks) were 0.86 and 14.04%, respectively. The model was further built into the ToxComp system, the ToxIVIVE tool specialized for cardiotoxicity assessment of drugs. Copyright © 2012 John Wiley & Sons, Ltd.