title: Prediction of Thorough QT study results using action potential simulations based on ion channel screens.
abstract: BACKGROUND
  Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development.
  
  
  METHODS
  Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations.
  
  
  RESULTS
  We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity.
  
  
  CONCLUSIONS
  This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment.
authors.affiliation: Computational Biology, Dept. of Computer Science, University of Oxford, Oxford OX1 3QD, UK. Electronic address: gary.mirams@cs.ox.ac.uk.
authors.forename: Gary R
authors.initials: GR
authors.lastname: Mirams
authors.affiliation: Clinical Informatics, R&D Information, AstraZeneca, Alderley Park, SK10 4TG, UK.
authors.forename: Mark R
authors.initials: MR
authors.lastname: Davies
authors.affiliation: Screening & Compound Profiling, GlaxoSmithKline, Stevenage SG1 2NY, UK.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Brough
authors.affiliation: Discovery Sciences, AstraZeneca, Alderley Park, SK10 4TG, UK.
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Bridgland-Taylor
authors.affiliation: Safety Evaluation and Risk Management, Global Clinical Safety, GlaxoSmithKline, Middlesex UB11 1BT, UK.
authors.forename: Yi
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Computational Biology, Dept. of Computer Science, University of Oxford, Oxford OX1 3QD, UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: Gavaghan
authors.affiliation: Translational Safety Department, Drug Safety & Metabolism, AstraZeneca, Alderley Park, SK10 4TG, UK.
authors.forename: Najah
authors.initials: N
authors.lastname: Abi-Gerges
mesh_terms: D000200:Action Potentials / Q000187:drug effects*; D004305:Dose-Response Relationship, Drug; D004353:Drug Evaluation, Preclinical / Q000009:adverse effects*; D004562:Electrocardiography / Q000187:drug effects*; D006801:Humans; D007473:Ion Channels / Q000378:metabolism*; D008133:Long QT Syndrome / Q000139:chemically induced*
chemical_list: D007473:Ion Channels
keywords: Action potential; Cardiac safety; Compound screening; High-throughput; Mathematical model; Methods; Thorough QT
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