Computational determination of hERG-related cardiotoxicity of drug candidates

Abstract

Background: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-ago go related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. Result: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when tenfold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. Conclusion: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach

“…Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect [ 89 ]. The evaluation of this parameter was by means of hERG ([ 17 ] takes into account the electro-affinity calculation (EA) of the compounds.…”

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…Thus, when the drug inhibits the hERG channel, it causes severe cardiac arrhythmia by druginduced QT prolongation. Recently, it also has been actively studied for data-driven in silico hERG-related toxicity prediction by many researchers [166][167][168][169][170][171][172][173]. Siramshetty et al [167] collected and preprocessed 5,804 compounds from the ChEMBL database for training and validated the model with literature-derived data.…”

Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

“…9,10 Also, these drugs induce cardiac muscle injury and modification in the normal functioning of the ion channels and pumps (voltage-gated sodium and potassium ion channel and Na þ -K þ ATPase pump). [11][12][13] Various drugs including antidepressants, antipsychotics, antihistamines, analgesics, opioids, beta-blockers, antiarrhythmics, ACE inhibitors, diuretics, etc. are known to contribute side-effects and toxicity by interacting with numerous receptors both centrally and peripherally resulting in cardiac deaths.…”

“…9,10 Also, these drugs induce cardiac muscle injury and modification in the normal functioning of the ion channels and pumps (voltage-gated sodium and potassium ion channel and Na + -K + ATPase pump). 11 –13…”

“…14,20,21 Lee et al showed hERG (human ether-a-go-go related gene)-related cardiotoxicity prediction using neural network models which achieved 80% accuracy, 60% sensitivity and 100% specificity. 12 Structure-based drug designing efforts of Angiotensin II receptor type 1 (AT1) antagonists identified high-affinity hERG1 blocking compounds as potential AT1 inhibitors. 13 These computational approaches derive relationships between different compounds through the physicochemical properties and its toxicological endpoints (e.g.…”