1
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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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2
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Kaitoh K, Yamanishi Y. Scaffold-Retained Structure Generator to Exhaustively Create Molecules in an Arbitrary Chemical Space. J Chem Inf Model 2022; 62:2212-2225. [DOI: 10.1021/acs.jcim.1c01130] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
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3
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Rishton GM, Look GC, Ni ZJ, Zhang J, Wang Y, Huang Y, Wu X, Izzo NJ, LaBarbera KM, Limegrover CS, Rehak C, Yurko R, Catalano SM. Discovery of Investigational Drug CT1812, an Antagonist of the Sigma-2 Receptor Complex for Alzheimer's Disease. ACS Med Chem Lett 2021; 12:1389-1395. [PMID: 34531947 DOI: 10.1021/acsmedchemlett.1c00048] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
An unbiased phenotypic neuronal assay was developed to measure the synaptotoxic effects of soluble Aβ oligomers. A collection of CNS druglike small molecules prepared by conditioned extraction was screened. Compounds that prevented and reversed synaptotoxic effects of Aβ oligomers in neurons were discovered to bind to the sigma-2 receptor complex. Select development compounds displaced receptor-bound Aβ oligomers, rescued synapses, and restored cognitive function in transgenic hAPP Swe/Ldn mice. Our first-in-class orally administered small molecule investigational drug 7 (CT1812) has been advanced to Phase II clinical studies for Alzheimer's disease.
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Affiliation(s)
- Gilbert M. Rishton
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Gary C. Look
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Zhi-Jie Ni
- Acme Bioscience, Inc., 3941 East Bayshore Road, Palo Alto, California 94303, United States
| | - Jason Zhang
- Acme Bioscience, Inc., 3941 East Bayshore Road, Palo Alto, California 94303, United States
| | - Yingcai Wang
- Acme Bioscience, Inc., 3941 East Bayshore Road, Palo Alto, California 94303, United States
| | - Yaodong Huang
- Acme Bioscience, Inc., 3941 East Bayshore Road, Palo Alto, California 94303, United States
| | - Xiaodong Wu
- Acme Bioscience, Inc., 3941 East Bayshore Road, Palo Alto, California 94303, United States
| | - Nicholas J. Izzo
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Kelsie M LaBarbera
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Colleen S. Limegrover
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Courtney Rehak
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Raymond Yurko
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
| | - Susan M. Catalano
- Cognition Therapeutics, 2403 Sidney Street, Suite 261, Pittsburgh, Pennsylvania 15203, United States
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4
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Ye Z, Yang W, Yang Y, Ouyang D. Interpretable machine learning methods for in vitro pharmaceutical formulation development. FOOD FRONTIERS 2021. [DOI: 10.1002/fft2.78] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
| | - Wenmian Yang
- State Key Laboratory of Internet of Things for Smart City University of Macau Macau China
| | - Yilong Yang
- School of Software Beihang University Beijing China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
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5
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Sekhar Pagadala N. Computational prediction of hERG blockers using homology modelling, molecular docking and QuaSAR studies. RESULTS IN CHEMISTRY 2021. [DOI: 10.1016/j.rechem.2021.100101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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6
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Wang Y, Huang L, Jiang S, Wang Y, Zou J, Fu H, Yang S. Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers. Front Pharmacol 2020; 10:1631. [PMID: 32063849 PMCID: PMC6997788 DOI: 10.3389/fphar.2019.01631] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/13/2019] [Indexed: 02/05/2023] Open
Abstract
Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.
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Affiliation(s)
- Yiwei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- College of Preclinical Medicine, Southwest Medical University, Luzhou, China
| | - Lei Huang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Basic Teaching Department, Sichuan College of Architectural Technology, Deyang, China
| | - Siwen Jiang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Zou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hongguang Fu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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7
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Munawar S, Windley MJ, Tse EG, Todd MH, Hill AP, Vandenberg JI, Jabeen I. Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities. Front Pharmacol 2018; 9:1035. [PMID: 30333745 PMCID: PMC6176658 DOI: 10.3389/fphar.2018.01035] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/27/2018] [Indexed: 12/17/2022] Open
Abstract
The hERG (human ether-a-go-go-related gene) encoded potassium ion (K+) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC50) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC50) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC50) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC50) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.
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Affiliation(s)
- Saba Munawar
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan.,Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Edwin G Tse
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Matthew H Todd
- School of Chemistry, The University of Sydney, Sydney, NSW, Australia
| | - Adam P Hill
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
| | | | - Ishrat Jabeen
- Research Center for Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan
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8
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Kalyaanamoorthy S, Barakat KH. Development of Safe Drugs: The hERG Challenge. Med Res Rev 2017; 38:525-555. [DOI: 10.1002/med.21445] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Revised: 02/04/2017] [Accepted: 03/16/2017] [Indexed: 02/06/2023]
Affiliation(s)
- Subha Kalyaanamoorthy
- Faculty of Pharmacy and Pharmaceutical Sciences; University Of Alberta; Edmonton Alberta Canada
| | - Khaled H. Barakat
- Faculty of Pharmacy and Pharmaceutical Sciences; University Of Alberta; Edmonton Alberta Canada
- Li Ka Shing Institute of Virology; University of Alberta; Edmonton Alberta Canada
- Li Ka Shing Applied Virology Institute; University of Alberta; Edmonton Alberta Canada
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9
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Gunio D, Froehlig J, Pappas K, Ferguson U, Wade H. Solution-Binding and Molecular Docking Approaches Combine to Provide an Expanded View of Multidrug Recognition in the MDR Gene Regulator BmrR. J Chem Inf Model 2016; 56:377-89. [DOI: 10.1021/acs.jcim.5b00704] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Drew Gunio
- Department
of Biophysics
and Biophysical Chemistry, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, Maryland 21205, United States
| | - John Froehlig
- Department
of Biophysics
and Biophysical Chemistry, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, Maryland 21205, United States
| | - Katerina Pappas
- Department
of Biophysics
and Biophysical Chemistry, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, Maryland 21205, United States
| | - Uneeke Ferguson
- Department
of Biophysics
and Biophysical Chemistry, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, Maryland 21205, United States
| | - Herschel Wade
- Department
of Biophysics
and Biophysical Chemistry, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, Maryland 21205, United States
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10
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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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11
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Abstract
The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure–activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).
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12
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A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity. Toxicol Lett 2014; 230:382-92. [PMID: 25127758 DOI: 10.1016/j.toxlet.2014.08.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Revised: 08/08/2014] [Accepted: 08/10/2014] [Indexed: 01/02/2023]
Abstract
Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model.
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13
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Novel Bayesian classification models for predicting compounds blocking hERG potassium channels. Acta Pharmacol Sin 2014; 35:1093-102. [PMID: 24976154 DOI: 10.1038/aps.2014.35] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 04/10/2014] [Indexed: 02/03/2023] Open
Abstract
AIM A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels. METHODS Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation. RESULTS A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation. CONCLUSION The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.
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14
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Patel BD, Ghate MD. Recent approaches to medicinal chemistry and therapeutic potential of dipeptidyl peptidase-4 (DPP-4) inhibitors. Eur J Med Chem 2014; 74:574-605. [PMID: 24531198 DOI: 10.1016/j.ejmech.2013.12.038] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 11/28/2013] [Accepted: 12/27/2013] [Indexed: 02/08/2023]
Abstract
Dipeptidyl peptidase-4 (DPP-4) is one of the widely explored novel targets for Type 2 diabetes mellitus (T2DM) currently. Research has been focused on the strategy to preserve the endogenous glucagon like peptide (GLP)-1 activity by inhibiting the DPP-4 action. The DPP-4 inhibitors are weight neutral, well tolerated and give better glycaemic control over a longer duration of time compared to existing conventional therapies. The journey of DPP-4 inhibitors in the market started from the launch of sitagliptin in 2006 to latest drug teneligliptin in 2012. This review is mainly focusing on the recent medicinal aspects and advancements in the designing of DPP-4 inhibitors with the therapeutic potential of DPP-4 as a target to convey more clarity in the diffused data.
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Affiliation(s)
- Bhumika D Patel
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Sarkhej-Gandhinagar Highway, Ahmedabad 382481, Gujarat, India.
| | - Manjunath D Ghate
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Sarkhej-Gandhinagar Highway, Ahmedabad 382481, Gujarat, India
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15
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Classification of blocker and non-blocker of hERG potassium ion channel using a support vector machine. Sci China Chem 2013. [DOI: 10.1007/s11426-013-4946-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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16
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Moorthy NSHN, Ramos MJ, Fernandes PA. Predictive QSAR models development and validation for human ether-a-go-go related gene (hERG) blockers using newer tools. J Enzyme Inhib Med Chem 2013; 29:317-24. [DOI: 10.3109/14756366.2013.779264] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
| | - Maria J. Ramos
- REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto
PortoPortugal
| | - Pedro A. Fernandes
- REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto
PortoPortugal
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17
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Di Martino GP, Masetti M, Ceccarini L, Cavalli A, Recanatini M. An Automated Docking Protocol for hERG Channel Blockers. J Chem Inf Model 2013; 53:159-75. [DOI: 10.1021/ci300326d] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Giovanni Paolo Di Martino
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Luisa Ceccarini
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Department of Drug Discovery
and Development, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
| | - Maurizio Recanatini
- Department of Pharmacy and Biotechnology,
Alma Mater Studiorum, Università di Bologna, Via Belmeloro 6, 40126 Bologna, Italy
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18
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Wang Z, Mussa HY, Lowe R, Glen RC, Yan A. Probability Based hERG Blocker Classifiers. Mol Inform 2012; 31:679-85. [DOI: 10.1002/minf.201200011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 07/03/2012] [Indexed: 11/11/2022]
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19
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Vandenberg JI, Perry MD, Perrin MJ, Mann SA, Ke Y, Hill AP. hERG K+ Channels: Structure, Function, and Clinical Significance. Physiol Rev 2012; 92:1393-478. [DOI: 10.1152/physrev.00036.2011] [Citation(s) in RCA: 463] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The human ether-a-go-go related gene (hERG) encodes the pore-forming subunit of the rapid component of the delayed rectifier K+ channel, Kv11.1, which are expressed in the heart, various brain regions, smooth muscle cells, endocrine cells, and a wide range of tumor cell lines. However, it is the role that Kv11.1 channels play in the heart that has been best characterized, for two main reasons. First, it is the gene product involved in chromosome 7-associated long QT syndrome (LQTS), an inherited disorder associated with a markedly increased risk of ventricular arrhythmias and sudden cardiac death. Second, blockade of Kv11.1, by a wide range of prescription medications, causes drug-induced QT prolongation with an increase in risk of sudden cardiac arrest. In the first part of this review, the properties of Kv11.1 channels, including biogenesis, trafficking, gating, and pharmacology are discussed, while the second part focuses on the pathophysiology of Kv11.1 channels.
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Affiliation(s)
- Jamie I. Vandenberg
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Matthew D. Perry
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Mark J. Perrin
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Stefan A. Mann
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Ying Ke
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
| | - Adam P. Hill
- Mark Cowley Lidwill Research Programme in Cardiac Electrophysiology, Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; St Vincent's Clinical School, University of New South Wales, New South Wales, Australia; and University of Ottawa Heart Institute, Ottawa, Canada
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20
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Moorthy NSHN, Ramos MJ, Fernandes PA. Analysis of van der Waals surface area properties for human ether-a-go-go-related gene blocking activity: computational study on structurally diverse compounds. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2012; 23:521-536. [PMID: 22452318 DOI: 10.1080/1062936x.2012.666264] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In the present investigation, a computational analysis was performed on a data set comprised of human ether-a-go-go-related gene (hERG) blockers (triethanolamine, 1,3-thiazol-2-yl and tetrasubstituted imidazoline derivatives) in order to investigate the structural features required to reduce the hERG-induced cardiotoxicity problems in an early stage of drug discovery. The results derived from the quantitative structure-activity relationship (QSAR) analysis showed that the volume, surface area and shape descriptors (vsurf_) contributed significantly in all the models. This reveals that the hydrogen-bonding and hydrophilicity properties (vsurf_HB1, vsurf_CW4 and a_acc) on the van der Waals (vdW) surface of the molecule is negatively contributed for the hERG blocking activity and the hydrophobic property (vsurf_D6) and the total polar volume (vsurf_Wp2) on the vdW surface of the molecule are favourable for the activity. Further, the pharmacophore analysis also shows that the Aro/Hyd/Acc contour is one of the important biophore sites for the hERG blocking activity. This suggests that the presence of aromatic, hydrophobic and hydrogen-bonding groups in the molecules is favourable for interaction. In comparison with our earlier works (explaining the role of topological and hydrophobicity properties for the hERG blocking activity), these studies provided additional information on the importance of vdW surface area properties for the hERG blocking activity. These results can be used with other molecular modelling studies for the design of novel molecules that are free of cardiotoxicity.
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Affiliation(s)
- N S H N Moorthy
- Requimte, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal.
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Su BH, Tu YS, Esposito EX, Tseng YJ. Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions. J Chem Inf Model 2012; 52:1660-73. [DOI: 10.1021/ci300060b] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Bo-Han Su
- Department
of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road,
Taipei, Taiwan 106
| | - Yi-shu Tu
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4,
Roosevelt Road, Taipei, Taiwan 106
| | | | - Yufeng J. Tseng
- Department
of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road,
Taipei, Taiwan 106
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4,
Roosevelt Road, Taipei, Taiwan 106
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22
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Jeon EH, Park JH, Jeong JH, Lee SK. 2D-QSAR analysis for hERG ion channel inhibitors. ANALYTICAL SCIENCE AND TECHNOLOGY 2011. [DOI: 10.5806/ast.2011.24.6.533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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23
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Du-Cuny L, Chen L, Zhang S. A critical assessment of combined ligand- and structure-based approaches to HERG channel blocker modeling. J Chem Inf Model 2011; 51:2948-60. [PMID: 21902220 DOI: 10.1021/ci200271d] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Blockade of human ether-à-go-go related gene (hERG) channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining quantitative structure-activity relationship (QSAR) modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high-predictive capacity and improved enrichment and could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed, and molecular docking studies were conducted, resulting in high correlations (R² = 0.81) between predicted and experimental pIC₅₀s. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities.
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Affiliation(s)
- Lei Du-Cuny
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas, M.D. Anderson Cancer Center, 1901 East Rd., Houston, Texas, USA
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24
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Abstract
The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the misapplication of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r(2), PRESS r(2), F-tests, etc.) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted end point values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a "rank order entropy" evaluation or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes and provides confidence in the use of a particular model within a specific domain of applicability.
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Shen MY, Su BH, Esposito EX, Hopfinger AJ, Tseng YJ. A Comprehensive Support Vector Machine Binary hERG Classification Model Based on Extensive but Biased End Point hERG Data Sets. Chem Res Toxicol 2011; 24:934-49. [DOI: 10.1021/tx200099j] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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26
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Venkatesh M, Wang H, Cayer J, Leroux M, Salvail D, Das B, Wrobel JE, Mani S. In vivo and in vitro characterization of a first-in-class novel azole analog that targets pregnane X receptor activation. Mol Pharmacol 2011; 80:124-35. [PMID: 21464197 DOI: 10.1124/mol.111.071787] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The pregnane X receptor (PXR) is a master regulator of xenobiotic clearance and is implicated in deleterious drug interactions (e.g., acetaminophen hepatotoxicity) and cancer drug resistance. However, small-molecule targeting of this receptor has been difficult; to date, directed synthesis of a relatively specific PXR inhibitor has remained elusive. Here we report the development and characterization of a first-in-class novel azole analog [1-(4-(4-(((2R,4S)-2-(2,4-difluorophenyl)-2-methyl-1,3-dioxolan-4-yl)methoxy)phenyl)piperazin-1-yl)ethanone (FLB-12)] that antagonizes the activated state of PXR with limited effects on other related nuclear receptors (i.e., liver X receptor, farnesoid X receptor, estrogen receptor α, peroxisome proliferator-activated receptor γ, and mouse constitutive androstane receptor). We investigated the toxicity and PXR antagonist effect of FLB-12 in vivo. Compared with ketoconazole, a prototypical PXR antagonist, FLB-12 is significantly less toxic to hepatocytes. FLB-12 significantly inhibits the PXR-activated loss of righting reflex to 2,2,2-tribromoethanol (Avertin) in vivo, abrogates PXR-mediated resistance to 7-ethyl-10-hydroxycamptothecin (SN-38) in colon cancer cells in vitro, and attenuates PXR-mediated acetaminophen hepatotoxicity in vivo. Thus, relatively selective targeting of PXR by antagonists is feasible and warrants further investigation. This class of agents is suitable for development as chemical probes of PXR function as well as potential PXR-directed therapeutics.
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Affiliation(s)
- Madhukumar Venkatesh
- Albert Einstein Cancer Center, Albert Einstein College of Medicine, New York, New York, USA
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27
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Moorthy NHN, Ramos MJ, Fernandes PA. hERG binding feature analysis of structurally diverse compounds by QSAR and fragmental analysis. RSC Adv 2011. [DOI: 10.1039/c1ra00131k] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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28
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Hecht D. Applications of machine learning and computational intelligence to drug discovery and development. Drug Dev Res 2010. [DOI: 10.1002/ddr.20402] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- David Hecht
- Southwestern College, Chula Vista, California
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Hajjo R, Grulke C, Golbraikh A, Setola V, Huang XP, Roth BL, Tropsha A. Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs. J Med Chem 2010; 53:7573-86. [PMID: 20958049 PMCID: PMC3438292 DOI: 10.1021/jm100600y] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Some antipsychotic drugs are known to cause valvular heart disease by activating serotonin 5-HT(2B) receptors. We have developed and validated binary classification QSAR models capable of predicting potential 5-HT(2B) actives. The classification accuracies of the models built to discriminate 5-HT(2B) actives from the inactives were as high as 80% for the external test set. These models were used to screen in silico 59,000 compounds included in the World Drug Index, and 122 compounds were predicted as actives with high confidence. Ten of them were tested in radioligand binding assays and nine were found active, suggesting a success rate of 90%. All validated actives were then tested in functional assays, and one compound was identified as a true 5-HT(2B) agonist. We suggest that the QSAR models developed in this study could be used as reliable predictors to flag drug candidates that are likely to cause valvulopathy.
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Affiliation(s)
- Rima Hajjo
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Christopher Grulke
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Alexander Golbraikh
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Vincent Setola
- National Institute of Mental Health Psychoactive Drug Screening Program, Division of Medicinal Chemistry and Natural Products and Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Xi-Ping Huang
- National Institute of Mental Health Psychoactive Drug Screening Program, Division of Medicinal Chemistry and Natural Products and Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Bryan L. Roth
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
- National Institute of Mental Health Psychoactive Drug Screening Program, Division of Medicinal Chemistry and Natural Products and Department of Pharmacology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Alexander Tropsha
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
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30
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Su BH, Shen MY, Esposito EX, Hopfinger AJ, Tseng YJ. In Silico Binary Classification QSAR Models Based on 4D-Fingerprints and MOE Descriptors for Prediction of hERG Blockage. J Chem Inf Model 2010; 50:1304-18. [DOI: 10.1021/ci100081j] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bo-Han Su
- Department of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, exeResearch, LLC, 32 University Drive, East Lansing, Michigan 48823, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, Illinois 60045, and College of Pharmacy MSC09 5360, 1 University of New Mexico, Albuquerque, New Mexico
| | - Meng-yu Shen
- Department of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, exeResearch, LLC, 32 University Drive, East Lansing, Michigan 48823, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, Illinois 60045, and College of Pharmacy MSC09 5360, 1 University of New Mexico, Albuquerque, New Mexico
| | - Emilio Xavier Esposito
- Department of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, exeResearch, LLC, 32 University Drive, East Lansing, Michigan 48823, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, Illinois 60045, and College of Pharmacy MSC09 5360, 1 University of New Mexico, Albuquerque, New Mexico
| | - Anton J. Hopfinger
- Department of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, exeResearch, LLC, 32 University Drive, East Lansing, Michigan 48823, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, Illinois 60045, and College of Pharmacy MSC09 5360, 1 University of New Mexico, Albuquerque, New Mexico
| | - Yufeng J. Tseng
- Department of Computer Science and Information Engineering, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, exeResearch, LLC, 32 University Drive, East Lansing, Michigan 48823, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No.1 Sec.4, Roosevelt Road, Taipei, Taiwan 106, The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, Illinois 60045, and College of Pharmacy MSC09 5360, 1 University of New Mexico, Albuquerque, New Mexico
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31
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Thai KM, Windisch A, Stork D, Weinzinger A, Schiesaro A, Guy R, Timin E, Hering S, Ecker G. The hERG Potassium Channel and Drug Trapping: Insight from Docking Studies with Propafenone Derivatives. ChemMedChem 2010; 5:436-42. [DOI: 10.1002/cmdc.200900374] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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32
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Hidaka S, Yamasaki H, Ohmayu Y, Matsuura A, Okamoto K, Kawashita N, Takagi T. Nonlinear classification of hERG channel inhibitory activity by unsupervised classification method. J Toxicol Sci 2010; 35:393-9. [DOI: 10.2131/jts.35.393] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
| | | | | | - Akiko Matsuura
- Graduate School of Pharmaceutical Sciences, Osaka University
| | - Kousuke Okamoto
- Graduate School of Pharmaceutical Sciences, Osaka University
| | - Norihito Kawashita
- Graduate School of Pharmaceutical Sciences, Osaka University
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University
- Research Collaboration Center on Emerging and Re-emerging Infections
| | - Tatsuya Takagi
- Graduate School of Pharmaceutical Sciences, Osaka University
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University
- Research Collaboration Center on Emerging and Re-emerging Infections
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33
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Raschi E, Ceccarini L, De Ponti F, Recanatini M. hERG-related drug toxicity and models for predicting hERG liability and QT prolongation. Expert Opin Drug Metab Toxicol 2009; 5:1005-21. [PMID: 19572824 DOI: 10.1517/17425250903055070] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND hERG K(+) channels have been recognized as a primary antitarget in safety pharmacology. Their blockade, caused by several drugs with different therapeutic indications, may lead to QT prolongation and, eventually, to potentially fatal arrhythmia, namely torsade de pointes. Therefore, a number of preclinical models have been developed to predict hERG liability early in the drug development process. OBJECTIVE The aim of this review is to outline the present state of the art on drug-induced hERG blockade, providing insights on the predictive value of in vitro and in silico models for hERG liability. METHODS On the basis of latest reports, high-throughput preclinical models have been discussed outlining advantages and limitations. CONCLUSION Although no single model has an absolute value, an integrated risk assessment is recommended to predict the pro-arrhythmic risk of a given drug. This prediction requires expertise from different areas and should encompass emerging issues such as interference with hERG trafficking and QT shortening.
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Affiliation(s)
- Emanuel Raschi
- University of Bologna, Department of Pharmacology, Italy
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34
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Coi A, Massarelli I, Saraceno M, Carli N, Testai L, Calderone V, Bianucci AM. Quantitative Structure-Activity Relationship Models for Predicting Biological Properties, Developed by Combining Structure- and Ligand-Based Approaches: An Application to the Human Ether-a-go-go-Related Gene Potassium Channel Inhibition. Chem Biol Drug Des 2009; 74:416-33. [DOI: 10.1111/j.1747-0285.2009.00873.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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35
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Dearden JC, Cronin MTD, Kaiser KLE. How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2009; 20:241-66. [PMID: 19544191 DOI: 10.1080/10629360902949567] [Citation(s) in RCA: 289] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Although thousands of quantitative structure-activity and structure-property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.
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Affiliation(s)
- J C Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK.
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36
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Abstract
Topological polar surface area (TPSA), which makes use of functional group contributions based on a large database of structures, is a convenient measure of the polar surface area that avoids the need to calculate ligand 3D structure or to decide which is the relevant biological conformation or conformations. We demonstrate the utility of TPSA in 2D-QSAR for 14 sets of diverse pharmacological activity data. Even though a large pool of reports showing the importance of the classic 2D descriptors such as calculated logP (ClogP) and calculated molar refractivity (CMR) exists in the 2D-QSAR literature, this is the first report to demonstrate the value of TPSA as a relevant descriptor applicable to a large, structurally and pharmacologically diverse set of classes of compounds. We also address the limitations of applicability of this descriptor for 2D-QSAR analysis. We observed a negative correlation of TPSA with activity data for anticancer alkaloids, MT1 and MT2 agonists, MAO-B and tumor necrosis factor-alpha inhibitors and a positive correlation with inhibitory activity data for telomerase, PDE-5, GSK-3, DNA-PK, aromatase, malaria, trypanosomatids and CB2 agonists.
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Affiliation(s)
- S Prasanna
- Department of Medicinal Chemistry, University of Mississippi, MS 38677-1848, USA
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37
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Thai KM, Ecker GF. Classification Models for hERG Inhibitors by Counter-Propagation Neural Networks. Chem Biol Drug Des 2008; 72:279-89. [DOI: 10.1111/j.1747-0285.2008.00705.x] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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38
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Prediction of hERG Potassium Channel Blockade Using kNN-QSAR and Local Lazy Regression Methods. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200810072] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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39
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Wang M, Yang XG, Xue Y. Identifying hERG Potassium Channel Inhibitors by Machine Learning Methods. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200810015] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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40
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Kawai K, Fujishima S, Takahashi Y. Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines. J Chem Inf Model 2008; 48:1152-60. [DOI: 10.1021/ci7004753] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Kentaro Kawai
- Laboratory for Molecular Information Systems, Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Tempaku-cho, Toyohashi 441-8580, Japan
| | - Satoshi Fujishima
- Laboratory for Molecular Information Systems, Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Tempaku-cho, Toyohashi 441-8580, Japan
| | - Yoshimasa Takahashi
- Laboratory for Molecular Information Systems, Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Tempaku-cho, Toyohashi 441-8580, Japan
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41
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Recanatini M, Cavalli A. QSAR and Pharmacophores for Drugs Involved in hERG Blockage. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/9783527621460.ch5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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42
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Abstract
The aim of this current review is to summarize the present status of pharmacokinetics in Drug Discovery. The review is structured into four sections. The first section is a general overview of what we understand by pharmacokinetics and the different LADMET aspects: Liberation, Absorption, Distribution, Metabolism, Excretion, and Toxicity. The second section highlights the different computational or in silico approaches to estimate/predict one or several aspects of the pharmacokinetic profile of a discovery lead compound. The third section discusses the most commonly used in vitro methodologies. The fourth and last section examines the various approaches employed towards the pharmacokinetic assessment of discovery molecules; including all the LADME processes, discussing the different mathematical methodologies available to establish the PK profile of a test compound; what the main differences are and what should be the criteria for using one or another mathematical approach. The major conclusion of this review is that the use of the appropriate preclinical assays has a key role in the long-term viability of a pharmaceutical company since applying the right tools early in discovery will play a key role in determining the company's ability to discover novel safe and effective therapeutics to patients as quickly as possible.
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Affiliation(s)
- Ana Ruiz-Garcia
- Pharmacokinetics and Drug Metabolism, Amgen, Inc, 1201 Amgen Court West, Seattle, Washington 98119, USA.
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43
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Inanobe A, Kamiya N, Murakami S, Fukunishi Y, Nakamura H, Kurachi Y. In Silico Prediction of the Chemical Block of Human Ether-a-Go-Go-Related Gene (hERG) K+ Current. J Physiol Sci 2008. [DOI: 10.2170/physiolsci.rv011408] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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44
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Du L, Li M, You Q, Xia L. A novel structure-based virtual screening model for the hERG channel blockers. Biochem Biophys Res Commun 2007; 355:889-94. [PMID: 17331468 DOI: 10.1016/j.bbrc.2007.02.068] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2007] [Accepted: 02/09/2007] [Indexed: 11/15/2022]
Abstract
The hERG potassium channel is a key effector of cardiac repolarization and the blockade of this channel could cause arrhythmia. Thus, hERG channel blockade plays an important role for the potential pro-arrhythmic liability. In this report, binding of blockers to the hERG potassium channel is investigated using a combination of homology modeling, molecular docking, and molecular simulations, where blockade activities are evaluated using the linear regression model of GoldScore fitness. This structure-based virtual screening model is able to estimate the pIC(50) value of a wide range of ligands for the hERG potassium channel. The docked poses for ligands are also consistent with published mutation. Therefore, this model for the prediction of hERG channel blockade has the potential to provide cost-effective virtual screening tools for the evaluation of the cardiac liability of new chemical entities.
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Affiliation(s)
- Lupei Du
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, China
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