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Related Experiment Videos

A model for identifying HERG K+ channel blockers.

Alex M Aronov1, Brian B Goldman

  • 1Vertex Pharmaceuticals Inc., Cambridge, MA 02139-4242, USA. alex_aronov@vrtx.com

Bioorganic & Medicinal Chemistry
|April 15, 2004
PubMed
Summary

A new computational model accurately identifies drugs that block cardiac HERG channels, a common cause of acquired Long QT Syndrome (LQTS). This tool aids early drug discovery by filtering potential HERG blockers.

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Cardiovascular drug safety

Background:

  • Acquired Long QT Syndrome (LQTS) is a frequent adverse effect of medications blocking cardiac HERG K+ channels.
  • Numerous diverse compounds inhibit HERG K+ current, posing a risk during drug development.

Purpose of the Study:

  • To develop an in silico tool for early identification of potential HERG channel blockers.
  • To create a predictive model for filtering compounds in virtual drug screening.

Main Methods:

  • A binary classification model was developed.
  • The model integrates a 2D topological similarity filter.
  • A 3D pharmacophore ensemble procedure was employed to classify HERG blockers.

Main Results:

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  • The model achieved an overall accuracy of 82% in discriminating HERG actives from inactives.
  • False negative and false positive rates were 29% and 15%, respectively.
  • The developed model demonstrates high predictive performance.

Conclusions:

  • The computational model is effective for virtual library counterscreening against HERG.
  • This tool can significantly aid in the early stages of drug discovery to mitigate LQTS risks.
  • The model offers a reliable method for assessing HERG channel inhibition potential.