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Related Experiment Video

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Indexing molecules for their hERG liability.

Anwar Rayan1, Mizied Falah, Jamal Raiyn

  • 1Drug Discovery Informatics Lab, QRC-Qasemi Research Center, Israel. a_rayan@qsm.ac.il

European Journal of Medicinal Chemistry
|June 4, 2013
PubMed
Summary

Predicting drug-induced blockage of the human Ether-a-go-go-Related-Gene (hERG) potassium channel is crucial for drug safety. A new hERG Toxicity Index (ETI) model accurately estimates molecular risk, aiding early drug discovery.

Keywords:
ACDCMCCardio-toxicityComputational chemistryEFETIHTSISEIterative Stochastic EliminationIterative Stochastic Elimination (ISE)MCCMDDRMDL drug data reportMatthews' Correlation CoefficientStructure activity relationsavailable chemicals directorycomprehensive medicinal chemistry databaseenrichment factorhERGhERG Toxicity Indexhigh-throughput screeninghuman ether-a-go-go-related gene

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Area of Science:

  • Cardiovascular Pharmacology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Drug-induced blockage of the human Ether-a-go-go-Related-Gene (hERG) potassium channel can lead to fatal cardiac arrhythmias.
  • Early prediction of hERG channel liability is essential for safe drug development, but in vitro assays are resource-intensive.

Purpose of the Study:

  • To develop a computational model for predicting drug liability to the hERG potassium channel.
  • To introduce the hERG Toxicity Index (ETI) for estimating molecular risk of hERG channel blockade.

Main Methods:

  • Application of the Iterative Stochastic Elimination (ISE) algorithm to build rule-based models.
  • Combination of models to create the hERG Toxicity Index (ETI).
  • Validation using internal and external datasets, including a large set from chEMBL.

Main Results:

  • The developed ETI model achieved an Area Under the Curve (AUC) of 0.94.
  • The model demonstrated excellent performance on an external test set with a Matthews Correlation Coefficient (MCC) of 0.89.
  • Distinct ETI values were observed for hERG binders, clinical drugs, and endogenous molecules.

Conclusions:

  • The ETI model provides an effective computational strategy for assessing hERG channel liability.
  • This approach can significantly improve drug candidate selection and the development of safer pharmaceutical products.
  • The ETI model is suitable for evaluating chemicals during hit/lead optimization stages.