hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties

Abstract

The human ether-a-go-go-related gene (hERG) potassium channel is a well-known contributor to drug-induced cardiotoxicity and therefore is an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. The availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect of using structure-based simulation and docking approaches for hERG drug liability predictions. In recent times, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson’s R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving a diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to the blockade, thereby facilitating the rational ligand design to minimize hERG liability.

GPU‐specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations

“…An important feature of the SILCS methodology is the use of GCMC sampling allowing for improved sampling of water and solutes throughout the simulation systems over that, which can occur versus the use of MD alone along with the ability to sample very deep and fully occluded pockets on the interior of proteins. To further improve the GCMC acceptance rates for solutes, we previously developed the oscillating μ ex GCMC approach, 8 which has been used successfully in a number of studies 43 . Accordingly, taking advantage of the improved sampling and computational efficiency associated with the GPU implementation described above, the oscillating μ ex GCMC methodology was enhanced to achieve improved sampling and control of the solute concentrations.…”

“…( for solutes, we previously developed the oscillating μ ex GCMC approach, 8 which has been used successfully in a number of studies. 43 Accordingly, taking advantage of the improved sampling and computational efficiency associated with the GPU implementation described above, the oscillating μ ex GCMC methodology was enhanced to achieve improved sampling and control of the solute concentrations.…”

In Silico Approach: Docking Study of Compounds in Ardisia Plant as COX-2 Inhibitor and Its Comparison with Existing Therapeutic Drugs

“…Literatures note that drugs to inhibit COX-2 enzyme have the potency to promote cardiovascular side effects [5,6]. An approach to explore cardiotoxicity is through hERG potassium channel, which plays an eminent role in cardiac function, conducting electrical activity in the heart [23,24]. If the inhibition hERG channel occurs, it can cause QT (a measured time of a cycle in cardiogram between wave Q and wave T) interval prolongation, which can increase cardiac arrhythmias or torsades de pointes (TDP) [25].…”

Exploring activity landscapes with extended similarity: is Tanimoto enough?

“…Recently, we proposed a new class of similarity indices: extended similarity indices [17][18][19], that allow comparing any number of molecules at the same time, with a much more attractive O(N) scaling. These new measures have been successfully applied in clustering tasks [20], studying phylogenetic trees [19], epigeneticfocused libraries [21], in feature selection [22], sampling of molecular dynamics simulations [23], and many other studies [24][25][26][27]. More notably, it has been shown that these indices can provide a very efficient way to quantify the chemical diversity of large libraries [28].…”