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

Abstract

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.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

“…A curated database of 207 structurally diverse hERG inhibitors as described by Munawar et al [29] was divided into 166 training (80%) and 41 test compounds (20%) by the diverse subset selection method (see Supplementary Information Dataset.csv). Two test sets namely internal test set (20% compounds of the whole data set) and external test set (latest hERG inhibition data gathered from literature.…”

“…Most recent hERG inhibition experimental data of 30 inhibitors reported in the year 2017 [38,39,40,41] and 2018 [29,42] including malarial compounds tested against hERG from Open Source Malaria (OSM) database [43] was gathered to evaluate the performance of the models (see Supplementary Information Table S2). Compounds with known hERG IC 50 values were selected for further evaluation and predictions of all four models.…”

“…The final pose of each ligand was selected on the basis of least energy score for further ligand-protein interaction analysis. A comparative ligand–protein interaction profile analysis has been performed on selected ligands by lipophilic and ligand efficiency profiling as proposed by Munawar et al [29].…”

“…The internal test set contained 20 percent (41 compounds) of the overall dataset generated using diverse subset selection procedure. For the external test set all compounds tested against hERG reported in the years 2017 [38,39,40,41] and 2018 [29,42], and the compounds available at Open Source Malaria (OSM) database [43] test against hERG liability were also included. In the data set various filters were applied to reduce the data size such as drug-likeness of compounds rule of five, molecular weight (between 200 and 700 KD), electrophysiology assay (patch-clamp), absolute activity values (pIC 50 ).…”

Computational determination of hERG-related cardiotoxicity of drug candidates

“…Thus, it is necessary to develop a computational approach to accelerate drug discovery. In recent years, several ligand-based in silico models have been developed to predict drug-hERG interactions based on the pharmacophore, quantitative structure-activity relationship (QSAR), and classification models [5][6][7][8].…”