Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

• Main Authors: Saba Munawar, Monique J. Windley, Edwin G. Tse, Matthew H. Todd, Adam P. Hill, Jamie I. Vandenberg, Ishrat Jabeen
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-09-01
• Series: Frontiers in Pharmacology
• Subjects: hERG inhibitors; trosade de pointes; long QT syndrom; pharmcophore; GRIND; molecular docking
• Online Access: https://www.frontiersin.org/article/10.3389/fphar.2018.01035/full

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.