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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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2
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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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3
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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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4
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Fraczkiewicz R, Lobell M, Göller AH, Krenz U, Schoenneis R, Clark RD, Hillisch A. Best of Both Worlds: Combining Pharma Data and State of the Art Modeling Technology To Improve in Silico pKa Prediction. J Chem Inf Model 2014; 55:389-97. [DOI: 10.1021/ci500585w] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Robert Fraczkiewicz
- Simulations Plus, Inc. 42505 10th
Street West, Lancaster, California 93534, United States
| | - Mario Lobell
- Global
Drug Discovery, Bayer Pharma AG, Wuppertal, Germany
| | | | - Ursula Krenz
- Global
Drug Discovery, Bayer Pharma AG, Wuppertal, Germany
| | | | - Robert D. Clark
- Simulations Plus, Inc. 42505 10th
Street West, Lancaster, California 93534, United States
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5
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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1053] [Impact Index Per Article: 105.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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6
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Kramer C, Fuchs JE, Whitebread S, Gedeck P, Liedl KR. Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty. J Med Chem 2014; 57:3786-802. [DOI: 10.1021/jm500317a] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Christian Kramer
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julian E. Fuchs
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Steven Whitebread
- Preclinical
Safety Profiling, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Peter Gedeck
- Novartis Institute for Tropical Diseases, 10 Biopolis Road, No. 05-01 Chromos, Singapore 138670, Singapore
| | - Klaus R. Liedl
- Department
of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center
for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
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7
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Abstract
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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Affiliation(s)
- Gregory Sliwoski
- Jr., Center for Structural Biology, 465 21st Ave South, BIOSCI/MRBIII, Room 5144A, Nashville, TN 37232-8725.
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8
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Affiliation(s)
- Paul Czodrowski
- Merck KGaA, Small Molecule
Platform, Global Computational Chemistry, Frankfurter Strasse 250,
64293 Darmstadt, Germany
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9
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Kireeva N, Kuznetsov SL, Bykov AA, Tsivadze AY. Towards in silico identification of the human ether-a-go-go-related gene channel blockers: discriminative vs. generative classification models. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2012; 24:103-117. [PMID: 23152964 DOI: 10.1080/1062936x.2012.742135] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
HERG potassium channels have a critical role in the normal electrical activity of the heart. The blockade of hERG channels in heart cells can result in a potentially fatal disorder called long QT syndrome. HERG channels can be blocked by compounds with diverse structures belonging to several drug classes. Presented herein are generative (Generative Topographic Maps) and discriminative (Support Vector Machines) classification models to categorize the compounds in silico into active and inactive classes by using different types of descriptors. The predictive performance of discriminative and generative classification models has been compared. Here, the possibility of using Generative Topographic Maps as an approach for applicability domain analysis and to generate probability-based descriptors was demonstrated to our knowledge for the first time. Comparison of obtained results with the models developed by other teams on the same data set has been performed.
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Affiliation(s)
- N Kireeva
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, Russia.
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10
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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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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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12
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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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13
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Jang JW, Song CM, Choi KH, Cho YS, Baek DJ, Shin KJ, Pae AN. In silico Analysis on hERG Channel Blocking Effect of a Series of T-type Calcium Channel Blockers. B KOREAN CHEM SOC 2011. [DOI: 10.5012/bkcs.2011.32.1.251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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14
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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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15
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Cramer RD. Tautomers and topomers: challenging the uncertainties of direct physicochemical modeling. J Comput Aided Mol Des 2010; 24:617-20. [DOI: 10.1007/s10822-010-9330-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2010] [Accepted: 03/10/2010] [Indexed: 11/24/2022]
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Venkatraman V, Chakravarthy PR, Kihara D. Application of 3D Zernike descriptors to shape-based ligand similarity searching. J Cheminform 2009; 1:19. [PMID: 20150998 PMCID: PMC2820497 DOI: 10.1186/1758-2946-1-19] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2009] [Accepted: 12/17/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The identification of promising drug leads from a large database of compounds is an important step in the preliminary stages of drug design. Although shape is known to play a key role in the molecular recognition process, its application to virtual screening poses significant hurdles both in terms of the encoding scheme and speed. RESULTS In this study, we have examined the efficacy of the alignment independent three-dimensional Zernike descriptor (3DZD) for fast shape based similarity searching. Performance of this approach was compared with several other methods including the statistical moments based ultrafast shape recognition scheme (USR) and SIMCOMP, a graph matching algorithm that compares atom environments. Three benchmark datasets are used to thoroughly test the methods in terms of their ability for molecular classification, retrieval rate, and performance under the situation that simulates actual virtual screening tasks over a large pharmaceutical database. The 3DZD performed better than or comparable to the other methods examined, depending on the datasets and evaluation metrics used. Reasons for the success and the failure of the shape based methods for specific cases are investigated. Based on the results for the three datasets, general conclusions are drawn with regard to their efficiency and applicability. CONCLUSION The 3DZD has unique ability for fast comparison of three-dimensional shape of compounds. Examples analyzed illustrate the advantages and the room for improvements for the 3DZD.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Biological Sciences, Purdue University, 915 West State Street, West Lafayette, IN 47907, USA
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17
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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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18
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Herman Skolnik award symposium honoring Yvonne Martin. J Comput Aided Mol Des 2009; 23:831-6. [DOI: 10.1007/s10822-009-9310-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2009] [Accepted: 11/17/2009] [Indexed: 11/25/2022]
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19
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Hansen K, Rathke F, Schroeter T, Rast G, Fox T, Kriegl JM, Mika S. Bias-Correction of Regression Models: A Case Study on hERG Inhibition. J Chem Inf Model 2009; 49:1486-96. [DOI: 10.1021/ci9000794] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Katja Hansen
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Fabian Rathke
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Timon Schroeter
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Georg Rast
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Thomas Fox
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Jan M. Kriegl
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
| | - Sebastian Mika
- University of Technology, Berlin, Germany, Departments of Drug Discovery Support and Lead Discovery, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach a.d. Riss, Germany, and idalab GmbH, Berlin, Germany
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