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El Harchi A, Hancox JC. hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction. J Pharmacol Toxicol Methods 2023; 123:107293. [PMID: 37468081 DOI: 10.1016/j.vascn.2023.107293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/23/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.
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Affiliation(s)
- Aziza El Harchi
- School of Physiology and Pharmacology and Neuroscience, Biomedical Sciences Building, The University of Bristol, University Walk, Bristol BS8 1TD, UK.
| | - Jules C Hancox
- School of Physiology and Pharmacology and Neuroscience, Biomedical Sciences Building, The University of Bristol, University Walk, Bristol BS8 1TD, UK
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Das NR, Sharma T, Toropov AA, Toropova AP, Tripathi MK, Achary PGR. Machine-learning technique, QSAR and molecular dynamics for hERG-drug interactions. J Biomol Struct Dyn 2023; 41:13766-13791. [PMID: 37021352 DOI: 10.1080/07391102.2023.2193641] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/06/2023] [Indexed: 04/07/2023]
Abstract
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.
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Affiliation(s)
- Nilima Rani Das
- Department of CA, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Tripti Sharma
- School of Pharmaceutical Sciences, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
| | - Andrey A Toropov
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Alla P Toropova
- Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - P Ganga Raju Achary
- Department of Chemistry, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
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Mozafari Z, Arab Chamjangali M, Beglari M, Doosti R. The efficiency of ligand-receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network. Chem Biol Drug Des 2020; 96:812-824. [PMID: 32259386 DOI: 10.1111/cbdd.13690] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 02/15/2020] [Accepted: 03/15/2020] [Indexed: 11/28/2022]
Abstract
A new approach is introduced for the construction of a predictive quantitative structure-activity relationship model in which only ligand-receptor (LR) interaction features are used as relevant descriptors. This approach combines the benefit of the random forest (RF) as a new variable selection method with the intrinsic capability of the artificial neural network (ANN). The interaction information of the ligand-receptor (LR) complex was used as molecular docking descriptors. The most relevant descriptors were selected using the RF technique and used as inputs of ANN. The proposed RF ANN (RF-LM-ANN) method was optimized and then evaluated by the prediction of pEC50 for some of the azine derivatives as non-nucleoside reverse transcriptase inhibitors. RF-LM-ANN model under the optimal conditions was evaluated using internal (validation) and external test sets. The determination coefficients of the external test and validation sets were 0.88 and 0.89, respectively. The mean square deviation (MSE) values for the prediction of biological activities in the external test and validation sets were found to be 0.10 and 0.11, respectively. The results obtained demonstrated the good prediction ability and high generalizability of the proposed RF-LM-ANN model based on the MMDs alone.
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Affiliation(s)
- Zeinab Mozafari
- Department of Chemistry, Shahrood University of Technology, Shahrood, Iran
| | | | - Mozhgan Beglari
- Department of Chemistry, Shahrood University of Technology, Shahrood, Iran
| | - Rahele Doosti
- Department of Chemistry, Shahrood University of Technology, Shahrood, Iran
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Beglari M, Goudarzi N, Shahsavani D, Arab Chamjangali M, Mozafari Z. Combination of radial distribution functions as structural descriptors with ligand-receptor interaction information in the QSAR study of some 4-anilinoquinazoline derivatives as potent EGFR inhibitors. Struct Chem 2020. [DOI: 10.1007/s11224-020-01505-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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5
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Minovski N, Novič M. Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structure-based methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.
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Computational investigations of hERG channel blockers: New insights and current predictive models. Adv Drug Deliv Rev 2015; 86:72-82. [PMID: 25770776 DOI: 10.1016/j.addr.2015.03.003] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 01/13/2015] [Accepted: 03/04/2015] [Indexed: 01/08/2023]
Abstract
Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.
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Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015; 28:581-604. [PMID: 25808539 DOI: 10.1002/jmr.2471] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 12/11/2022]
Abstract
Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.
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Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jessica Holien
- ACRF Rational Drug Discovery Centre and Structural Biology Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Victoria, 3004, Australia.,Department of Surgery Austin Health, University of Melbourne, Melbourne, Victoria, 3084, Australia.,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Victoria, 3004, Australia.,School of Biomedical Sciences, CHIRI Biosciences, Curtin University, Perth, Western Australia, 6845, Australia
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Molbaek K, Scharff-Poulsen P, Helix-Nielsen C, Klaerke DA, Pedersen PA. High yield purification of full-length functional hERG K+ channels produced in Saccharomyces cerevisiae. Microb Cell Fact 2015; 14:15. [PMID: 25656388 PMCID: PMC4341239 DOI: 10.1186/s12934-015-0193-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 12/11/2014] [Indexed: 11/23/2022] Open
Abstract
The hERG potassium channel is essential for repolarization of the cardiac action potential. Due to this vital function, absence of unintended and potentially life-threatening interactions with hERG is required for approval of new drugs. The structure of hERG is therefore one of the most sought-after. To provide purified hERG for structural studies and new hERG biomimetic platforms for detection of undesirable interactions, we have developed a hERG expression platform generating unprecedented amounts of purified and functional hERG channels. Full-length hERG, with or without a C-terminally fused green fluorescent protein (GFP) His 8-tag was produced from a codon-optimized hERG cDNA in Saccharomyces cerevisiae. Both constructs complemented the high potassium requirement of a knock-out Saccharomyces cerevisiae strain, indicating correct tetramer assembly in vivo. Functionality was further demonstrated by Astemizole binding to membrane embedded hERG-GFP-His 8 with a stoichiometry corresponding to tetramer assembly. The 156 kDa hERG-GFP protein accumulated to a membrane density of 1.6%. Fluorescence size exclusion chromatography of hERG-GFP-His 8 solubilized in Fos-Choline-12 supplemented with cholesteryl-hemisuccinate and Astemizole resulted in a monodisperse elution profile demonstrating a high quality of the hERG channels. hERG-GFP-His 8 purified by Ni-affinity chromatography maintained the ability to bind Astemizole with the correct stoichiometry indicating that the native, tetrameric structure was preserved. To our knowledge this is the first reported high-yield production and purification of full length, tetrameric and functional hERG. This significant breakthrough will be paramount in obtaining hERG crystal structures, and in establishment of new high-throughput hERG drug safety screening assays.
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Affiliation(s)
- Karen Molbaek
- Department of Veterinary and Clinical Animal Science, University of Copenhagen, Dyrlaegevej 100, Frederiksberg, DK-1870, Denmark.
| | - Peter Scharff-Poulsen
- Department of Biology, University of Copenhagen, Universitetsparken 13, Copenhagen OE, DK- 2100, Denmark.
| | - Claus Helix-Nielsen
- Department of Environmental Engineering, Technical University of Denmark, Miljoevej building 113, Kgs Lyngby, 24105, Denmark. .,Aquaporin A/S, Ole Maaloesvej 3, Copenhagen N, DK-2200, Denmark. .,Laboratory for Water Biophysics and Membrane Technology, University of Maribor, Smetanova ulica 17, Maribor, SL-2000, Slovenia.
| | - Dan A Klaerke
- Department of Veterinary and Clinical Animal Science, University of Copenhagen, Dyrlaegevej 100, Frederiksberg, DK-1870, Denmark.
| | - Per Amstrup Pedersen
- Department of Biology, University of Copenhagen, Universitetsparken 13, Copenhagen OE, DK- 2100, Denmark.
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Kratz JM, Schuster D, Edtbauer M, Saxena P, Mair CE, Kirchebner J, Matuszczak B, Baburin I, Hering S, Rollinger JM. Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools. J Chem Inf Model 2014; 54:2887-901. [PMID: 25148533 DOI: 10.1021/ci5001955] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.
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Affiliation(s)
- Jadel M Kratz
- Departamento de Ciências Farmacêuticas, Universidade Federal de Santa Catarina , 88.040-900 Florianópolis, Santa Catarina, Brazil
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