Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Abstract

Drug-induced blockade of the human ether-a-go-gorelated gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligandbased approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein−ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUC MAX = 0.86 ± 0.01) and negative predictive values (NPV MAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein−ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

“…Importantly the performed temporal validation confirms the reliability of our models in real-life cases, given their ability to properly classify as hERG blockers or nonblocker compounds belonging to a repository (ChEMBL ( Gaulton et al, 2012 ) v28) published after the data used for building TS and VS (ChEMBL ( Gaulton et al, 2012 ) v25). Noteworthily, the models can be efficiently used in combination with structure-based strategies ( Creanza et al, 2021 ) as testified by recent literature ( Mansouri et al, 2016 ; Kamel et al, 2017 ). Finally, the performed comparative analysis indicates that the top-performing consensus model herein developed outperforms several commonly employed classifiers available in the literature.…”

“…A total of 17,952 activity entries were extracted from ChEMBL ( Gaulton et al, 2012 ) v25 according to the Target ID (ChEMBL240) assigned to the hERG channel. To ensure data validity, the database was mined retaining only those entries matching the following criteria already suggested in the literature: 1) annotated exclusively with IC 50 (11,144 entries) measures, 2) referring to assays conducted on human targets (“target_organism” = “ Homo sapiens ”), 3) marked as direct binding (“assay_type” = “B”), and 4) free of warnings in the “data_validity_comment” field ( Alberga et al, 2019 ; Bosc et al, 2019 ; Creanza et al, 2021 ). SMILES were curated using a semiautomated in-house procedure described by Gadaleta et al (2018a) .…”

“…In this context, to avoid hERG liability during the DD process and, therefore, prioritize safe drug candidates at the early preclinical stage, the employment of in silico tools is highly desirable since in vitro (e.g., fluorescence-based assays, electrophysiology measurements, rubidium-flux assays, radioligand binding assays) and in vivo experiments are much more laborious, time-consuming, and expensive ( Priest et al, 2008 ; Jing et al, 2015 ). Accordingly, several in silico tools have been developed in the last few years using both ligand- and structure-based approaches ( Jing et al, 2015 ; Villoutreix and Taboureau, 2015 ; Kalyaanamoorthy and Barakat, 2018 ; Creanza et al, 2021 ). In the absence of an atomic-resolution hERG structure, the attention of both academia and industry has been mainly focused on the development of ligand-based classifiers (e.g., pharmacophore models, quantitative structure–activity relationship (QSAR) approaches) ( Jing et al, 2015 ; Villoutreix and Taboureau, 2015 ; Slavov et al, 2017 ; Sun et al, 2017 ; Kalyaanamoorthy and Barakat, 2018 ).…”

A Review of Computational Methods in Predicting hERG Channel Blockers

“…It is worth noting that a novel ML approach” AlphaFold” has revolutionized the accuracy of predicting protein structures, even in cases of unknown proteins [92] . In addition, the first structure‐based classifiers were proposed for predicting hERG liability without the limitations of ligand‐based methods, [93] providing new insights for screening hERG blockers.…”

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

“…Accordingly, a number of research groups have applied structure-based simulation and docking approaches in modeling ligand interactions with hERG channels, revealing the benefits the SBDD approach can offer when compared to ligand-based techniques [48][49][50][51][52][53][54][55][56][57]. A contribution in this direction was recently reported by Mangiatordi and coworkers for a large number of compounds, where they showed the utility of docking scores and their integration with protein-ligand interaction fingerprints [58].…”