title: Predictive models for HERG channel blockers: ligand-based and structure-based approaches.
abstract: Acquired long QT syndrome caused by drugs that block the human ether-a-go-go-related-gene (hERG) K(+) channel causes severe side effects and thus represents a major problem in clinical studies of drug candidates. Therefore, early prediction of hERG K(+) channel affinity of drug candidates is becoming increasingly important in the drug discovery process. Both structure-based and ligand-based approaches have been undertaken to shed more light on the molecular basis of drug-channel interaction. In this article, in silico approaches for prediction of interaction with hERG are reviewed. Special attention is drawn to the in vitro biological testing systems as well as to consensus approaches for improvement of predictive power.
authors.affiliation: Department of Medicinal Chemistry, University of Vienna; Althanstrasse 14, A-1090, Vienna, Austria.
authors.forename: Khac-Minh
authors.initials: KM
authors.lastname: Thai
authors.forename: Gerhard F
authors.initials: GF
authors.lastname: Ecker
mesh_terms: D003198:Computer Simulation; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D008024:Ligands; D008133:Long QT Syndrome / Q000139:chemically induced; D026902:Potassium Channel Blockers / Q000009:adverse effects* / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D051638:Ether-A-Go-Go Potassium Channels; D008024:Ligands; D026902:Potassium Channel Blockers