Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration

BACKGROUND AND OBJECTIVE

Assessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation.

METHODS

Experiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration.

RESULTS

The experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models.

CONCLUSIONS

These new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug-hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk

BACKGROUND AND PURPOSE

Drug-induced reduction of the rapid delayed rectifier potassium current carried by the human Ether-à-go-go-Related Gene (hERG) channel is associated with increased risk of arrhythmias. Recent updates to drug safety regulatory guidelines attempt to capture each drug's hERG binding mechanism by combining in vitro assays with in silico simulations. In this study, we investigate the impact on in silico proarrhythmic risk predictions due to uncertainty in the hERG binding mechanism and physiological hERG current model.

EXPERIMENTAL APPROACH

Possible pharmacological binding models were designed for the hERG channel to account for known and postulated small molecule binding mechanisms. After selecting a subset of plausible binding models for each compound through calibration to available voltage-clamp electrophysiology data, we assessed their effects, and the effects of different physiological models, on proarrhythmic risk predictions.

KEY RESULTS

For some compounds, multiple binding mechanisms can explain the same data produced under the safety testing guidelines, which results in different inferred binding rates. This can result in substantial uncertainty in the predicted torsade risk, which often spans more than one risk category. By comparison, we found that the effect of a different hERG physiological current model on risk classification was subtle.

CONCLUSION AND IMPLICATIONS

The approach developed in this study assesses the impact of uncertainty in hERG binding mechanisms on predictions of drug-induced proarrhythmic risk. For some compounds, these results imply the need for additional binding data to decrease uncertainty in safety-critical applications.

Machine-learning technique, QSAR and molecular dynamics for hERG-drug interactions

One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (in silico) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound's IC50 value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew's correlation coefficient formula and F1score were applied to compare and track the developed models' performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG-gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG-ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma.