In silico prediction of hERG inhibition

Future Medicinal Chemistry

Volumn 7, Issue 5, 2015, Pages 571-586

  Jing, Yankang ^a   Easter, Alison ^a   Peters, David ^a   Kim, Norman ^a   Enyedy, Istvan J ^a  

^a BIOGEN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LIGAND; POTASSIUM CHANNEL HERG; POTASSIUM CHANNEL BLOCKING AGENT;

ACTION POTENTIAL DURATION; COMPUTER MODEL; HUMAN; PHARMACOPHORE; POTASSIUM CURRENT; PREDICTION; PRIORITY JOURNAL; QT INTERVAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; ANTAGONISTS AND INHIBITORS; CHEMISTRY; COMPUTER AIDED DESIGN; COMPUTER SIMULATION; DRUG DEVELOPMENT; METABOLISM; PROCEDURES;

COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; DRUG DISCOVERY; ETHER-A-GO-GO POTASSIUM CHANNELS; HUMANS; POTASSIUM CHANNEL BLOCKERS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84930003941     PISSN: 17568919     EISSN: 17568927     Source Type: Journal    
DOI: 10.4155/fmc.15.18     Document Type: Review

References (103)

1. Tristani-Firouzi M, Chen J, Mitcheson JS, Sanguinetti MC. Molecular biology of K+ channels and their role in cardiac arrhythmias. Am. J. Med. 110(1), 50-59 (2001).
2. Shah RR. Can pharmacogenetics help rescue drugs withdrawn from the market? Pharmacogenomics 7(6), 889-908 (2006).
3. International Conference on Harmonisation; guidance on S7A safety pharmacology studies for human pharmaceuticals; availability. Notice. Fed. Regist. 66(135), 36791-36792 (2001).
4. International Conference on Harmonisation; guidance on S7B nonclinical evaluation of the potential for delayed ventricular repolarization (QT interval prolongation) by human pharmaceuticals; availability. Notice. Fed Regist 70(202), 61133-61134 (2005).
5. International Conference on Harmonisation; guidance on E14 clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for non-Antiarrhythmic drugs; availability. Notice. Fed Regist 70(202), 61134-61135 (2005).
6. Bridgland-Taylor MH, Hargreaves AC, Easter A et al. Optimization and validation of a medium-throughput electrophysiology-based hERG assay using IonWorks HT. J. Pharmacol. Toxicol. Methods 54(2), 189-199 (2006).
7. Mathes C, Friis S, Finley M, Liu Y. QPatch: the missing link between HTS and ion channel drug discovery. Comb. Chem. High Throughput Screen. 12(1), 78-95 (2009).
8. Chiu PJS, Marcoe KF, Bounds SE et al. Validation of a [3H] astemizole binding assay in HEK293 cells expressing HERG K+ channels. J. Pharmacol. Sci. (Tokyo) 95(3), 311-319 (2004).
9. Chaudhary KW, ONeal JM, Mo ZL, Fermini B, Gallavan RH, Bahinski A. Evaluation of the rubidium efflux assay for preclinical identification of hERG blockade. Assay Drug Dev. Technol. 4(1), 73-82 (2006).
10. Redfern WS, Carlsson L, Davis AS et al. Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. Cardiovasc. Res. 58(1), 32-45 (2003).
11. Milan DJ, Peterson TA, Ruskin JN, Peterson RT, MacRae CA. Drugs that induce repolarization abnormalities cause bradycardia in zebrafish. Circulation 107(10), 1355-1358 (2003).
12. Aronov AM. Predictive in silico modeling for hERG channel blockers. Drug Discov. Today 10(2), 149-155 (2005).
13. Braga RC, Alves VM, Silva MFB et al. Tuning hERG out: antitarget QSAR models for drug development. Curr. Top. Med. Chem. 14(11), 1399-1415 (2014).
14. Wang S, Li Y, Xu L, Li D, Hou T. Recent developments in computational prediction of hERG blockage. Curr. Top. Med. Chem. 13(11), 1317-1326 (2013).
15. Taboureau O, Jorgensen FS. In silico predictions of hERG channel blockers in drug discovery: from ligand-based and target-based approaches to systems chemical biology. Comb. Chem. High Throughput Screen. 14(5), 375-387 (2011).
16. Recanatini M, Cavalli A. QSAR and pharmacophores for drugs involved in hERG blockage. Methods Princ. Med. Chem. 38, 109-126 (2008).
17. Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design - a review. Curr. Top. Med. Chem. 10(1), 95-115 (2010).
18. Durdagi S, Subbotina J, Lees-Miller J, Guo J, Duff HJ, Noskov SY. Insights into the molecular mechanism of hERG1 channel activation and blockade by drugs. Curr. Med. Chem. 17(30), 3514-3532 (2010).
19. Keserü GM. Prediction of hERG potassium channel affinity by traditional and hologram qSAR methods. Bioorg. Med. Chem. Lett. 13(16), 2773-2775 (2003).
20. Coi A, Massarelli I, Murgia L, Saraceno M, Calderone V, Bianucci AM. Prediction of hERG potassium channel affinity by the CODESSA approach. Bioorg. Med. Chem. 14(9), 3153-3159 (2006).
21. Song M, Clark M. Development and Evaluation of an in silico model for hERG binding. J. Chem. Inf. Model. 46(1), 392-400 (2005).
22. Yoshida K, Niwa T. Quantitative structure-Activity relationship studies on inhibition of HERG potassium channels. J. Chem. Inf. Model. 46(3), 1371-1378 (2006).
23. Kramer C, Beck B, Kriegl J, Clark T. A composite model for hERG blockade. ChemMedChem 3(2), 254-265 (2008).
24. Garg D, Gandhi T, Gopi Mohan C. Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques. J. Mol. Graphics Model. 26(6), 966-976 (2008).
25. Hansen K, Rathke F, Schroeter T et al. Bias-correction of regression models: a case study on hERG inhibition. J. Chem. Inf. Model. 49(6), 1486-1496 (2009).
26. Sinha N, Sen S. Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model. Eur. J. Med. Chem. 46(2), 618-630 (2011).
27. Moorthy NSH, 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. 29(3), 317-324 (2014).
28. Aptula AO, Cronin MTD. Prediction of hERG K+ blocking potency: application of structural knowledge. SAR QSAR Environ. Res. 15(5-6), 399-411 (2004).
29. Schultz TW, Cronin MTD. Essential and desirable characteristics of ecotoxicity quantitative structure-Activity relationships. Environ. Toxicol. Chem. 22(3), 599-607 (2003).
30. Katritzky AR, Ignatchenko ES, Barcock RA, Lobanov VS, Karelson M. Prediction of gas chromatographic retention times and response factors using a general qualitative structureproperty relationships treatment. Anal. Chem. 66(11), 1799-1807 (1994).
31. Vapnik V, Chapelle O. Bounds on error expectation for support vector machines. Neural Comput. 12(9), 2013-2036 (2000).
32. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947-1958 (2003).
33. Pitzer EW. A review of four molecular structure indexing methods. Prepr. Am. Chem. Soc. Div. Pet. Chem. 32(2), 534-539 (1987).
34. Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA. Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl Chem 70 5 1129-1143 (1998).
35. Ekins S, Crumb WJ, Sarazan RD, Wikel JH, Wrighton SA. Three-dimensional quantitative structure-Activity relationship for inhibition of human ether-A-go-go-related gene potassium channel. J. Pharmacol. Exp. Ther. 301(2), 427-434 (2002).
36. Morgan TK Jr, Sullivan ME. An overview of class III electrophysiological agents: a new generation of antiarrhythmic therapy. Prog. Med. Chem. 29, 65-108 (1992).
37. Matyus P, Borosy AP, Varro A, Papp JG, Barlocco D, Cignarella G. Development of pharmacophores for inhibitors of the rapid component of the cardiac delayed rectifier potassium current. Int. J. Quantum Chem. 69(1), 21-30 (1998).
38. Aronov AM, Goldman BB. A model for identifying HERG K+ channel blockers. Bioorg. Med. Chem. 12(9), 2307-2315 (2004).
39. Aronov AM. Common pharmacophores for uncharged human ether-A-go-go-related gene (hERG) blockers. J. Med. Chem. 49(23), 6917-6921 (2006).
40. Aronov AM. Tuning out of hERG. Curr. Opin. Drug Discov. Dev.11(1), 128-140 (2008).
41. Peukert S, Brendel J, Pirard B et al. Pharmacophore-based search, synthesis, and biological evaluation of anthranilic amides as novel blockers of the Kv1.5 channel. Bioorg. Med. Chem. Lett. 14(11), 2823-2827 (2004).
42. Durdagi S, Duff HJ, Noskov SY. Combined receptor and ligand-based approach to the universal pharmacophore model development for studies of drug blockade to the hERG1 pore domain. J. Chem. Inf. Model. 51(2), 463-474 (2011).
43. Tan Y, Chen Y, You Q, Sun H, Li M. Predicting the potency of hERG K+ channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models. J. Mol. Model. 18(3), 1023-1036 (2012).
44. 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. 51(11), 2948-2960 (2011).
45. Kratz JM, Schuster D, Edtbauer M et al. Experimentally validated hERG pharmacophore models as cardiotoxicity prediction tools. J. Chem. Inf. Model. 54(10), 2887-2901 (2014).
46. Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Comput. Sci. 45(1), 160-169 (2005).
47. Strobl GR, von Kruedener S, Stoeckigt J, Guengerich FP, Wolff T. Development of a pharmacophore for inhibition of human liver cytochrome P-450 2D6: molecular modeling and inhibition studies. J. Med. Chem. 36(9), 1136-1145 (1993).
48. Ramesh M, Bharatam PV. CYP isoform specificity toward drug metabolism: analysis using common feature hypothesis. J. Mol. Model. 18(2), 709-720 (2012).
49. Güner OF, Bowen JP. Pharmacophore modeling for ADME. Curr. Top. Med. Chem. 13(11), 1327-1342 (2013).
50. Cavalli A, Poluzzi E, De Ponti F, Recanatini M. Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K+ channel blockers. J. Med. Chem. 45(18), 3844-3853 (2002).
51. Pearlstein RA, Vaz RJ, Kang J et al. Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches. Bioorg. Med. Chem. Lett. 13(10), 1829-1835 (2003).
52. Carosati E, Lemoine H, Spogli R et al. Binding studies and GRIND/ALMOND-based 3D QSAR analysis of benzothiazine type KATP-channel openers. Bioorg. Med. Chem. 13(19), 5581-5591 (2005).
53. Ermondi G, Visentin S, Caron G. GRIND-based 3D-QSAR and CoMFA to investigate topics dominated by hydrophobic interactions: the case of hERG K+ channel blockers. Eur. J. Med. Chem. 44(5), 1926-1932 (2009).
54. Du LP, Tsai KC, Li MY, You Qd, Xia L. The pharmacophore hypotheses of IKr potassium channel blockers: novel class III antiarrhythmic agents. Bioorg. Med. Chem. Lett. 14(18), 4771-4777 (2004).
55. Cianchetta G, Li Y, Kang J et al. Predictive models for hERG potassium channel blockers. Bioorg. Med. Chem. Lett. 15(15), 3637-3642 (2005).
56. Roche O, Trube G, Zuegge J, Pflimlin P, Alanine A, Schneider G. A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. ChemBioChem 3(5), 455-459 (2002).
57. Hidaka S, Yamasaki H, Ohmayu Y et al. Nonlinear classification of hERG channel inhibitory activity by unsupervised classification method. J. Toxicol. Sci. 35(3), 393-399 (2010).
58. Vracko M. Kohonen artificial neural network and counter propagation neural network in molecular structure-toxicity studies. Curr.Comput. Aided Drug Des. 1(1), 73-78 (2005).
59. Roche O, Trube G, Zuegge J, Pflimlin P, Alanine A, Schneider G. A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. ChemBioChem 3(5), 455-459 (2002).
60. Bains W, Basman A, White C. HERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study. Prog. Biophys. Mol. Biol. 86(2), 205-233 (2004).
61. Tobita M, Nishikawa T, Nagashima R. A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. Bioorg. Med. Chem. Lett. 15(11), 2886-2890 (2005).
62. Dubus E, Ijjaali I, Petitet F, Michel A. In silico classification of herg channel blockers: a knowledge-based strategy. ChemMedChem 1(6), 622-630 (2006).
63. Sun H. An accurate and interpretable bayesian classification model for prediction of hERG liability. ChemMedChem 1(3), 315-322 (2006).
64. Ekins S, Balakin KV, Savchuk N, Ivanenkov Y. Insights for human ether-A-go-go-related gene potassium channel inhibition using recursive partitioning and kohonen and sammon mapping techniques. J. Med. Chem. 49(17), 5059-5071 (2006).
65. Gepp MM, Hutter MC. Determination of hERG channel blockers using a decision tree. Bioorg. Med. Chem. 14(15), 5325-5332 (2006).
66. Wang M, Yang XG, Xue Y. Identifying hERG potassium channel inhibitors by machine learning methods. QSAR Comb. Sci. 27(8), 1028-1035 (2008).
67. Thai KM, Ecker GF. A binary QSAR model for classification of hERG potassium channel blockers. Bioorg. Med. Chem. 16(7), 4107-4119 (2008).
68. Jia L, Sun H. Support vector machines classification of hERG liabilities based on atom types. Bioorg. Med. Chem. 16(11), 6252-6260 (2008).
69. Li Q, Jorgensen FS, Oprea T, Brunak S, Taboureau O. hERG classification model based on a combination of support vector machine method and GRIND descriptors. Mol. Pharmaceutics 5(1), 117-127 (2008).
70. Chekmarev DS, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S, Welsh WJ. Shape signatures: new descriptors for predicting cardiotoxicity in silico. Chem. Res. Toxicol. 21(6), 1304-1314 (2008).
71. Filz O, Lagunin A, Filimonov D, Poroikov V. Computeraided prediction of QT-prolongation. SAR QSAR Environ. Res. 19(1-2), 81-90 (2008).
72. Thai KM, Ecker GF. Similarity-based SIBAR descriptors for classification of chemically diverse hERG blockers. Mol. Diversity 13(3), 321-336 (2009).
73. Nisius B, Göller AH. Similarity-based classifier using topomers to provide a knowledge base for hERG channel inhibition. J. Chem. Inf. Model. 49(2), 247-256 (2009).
74. 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. 50(7), 1304-1318 (2010).
75. Doddareddy MR, Klaasse EC, Shagufta, IJzerman AP, Bender A. Prospective validation of a comprehensive in silico herg model and its applications to commercial compound and drug databases. ChemMedChem 5(5), 716-729 (2010).
76. Wang S, Li Y, Wang J et al. ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of herg potassium channel blockage. Mol. Pharmaceutics 9(4), 996-1010 (2012).
77. Broccatelli F, Mannhold R, Moriconi A, Giuli S, Carosati E. QSAR modeling and data mining link torsades de pointes risk to the interplay of extent of metabolism, active transport, and hERG liability. Mol. Pharmaceutics 9(8), 2290-2301 (2012).
78. 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. 52(6), 1660-1673 (2012).
79. Czodrowski P. hERG me out. J. Chem. Inf. Model. 53(9), 2240-2251 (2013).
80. Labute P. A widely applicable set of descriptors. J. Mol. Graphics Model. 18(4/5), 464-477 (2000).
81. Cherkasov A. Inductive descriptors: 10 successful years in QSAR. Curr. Comput. Aided Drug Des. 1(1), 21-42 (2005).
82. Güner OF, Henry DR. Formula for determining the goodness of hit lists in 3D database searches. Network Sci. 4(6), (1998).
83. Cruciani G, Crivori P, Carrupt PA, Testa B. Molecular fields in quantitative structure-permeation relationships: the VolSurf approach. J. Mol. Struct. (Theochem) 503(1-2), 17-30 (2000).
84. Oesterberg F, Aqvist J. Exploring blocker binding to a homology model of the open hERG K+ channel using docking and molecular dynamics methods. FEBS Lett. 579(13), 2939-2944 (2005).
85. Stansfeld PJ, Gedeck P, Gosling M, Cox B, Mitcheson JS, Sutcliffe MJ. Drug block of the hERG potassium channel: insight from modeling. Proteins: Struct. Funct. Bioinf. 68(2), 568-580 (2007).
86. Subbotina J, Yarov-Yarovoy V, Lees-Miller J et al. Structural refinement of the hERG1 pore and voltage-sensing domains with ROSETTA-membrane and molecular dynamics simulations. Proteins: Struct. Funct. Bioinf. 78(14), 2922-2934 (2010).
87. Farid R, Day T, Friesner RA, Pearlstein RA. New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg. Med. Chem. 14(9), 3160-3173 (2006).
88. Masetti M, Cavalli A, Recanatini M. Modeling the hERG potassium channel in a phospholipid bilayer: molecular dynamics and drug docking studies. J. Comput. Chem. 29(5), 795-808 (2008).
89. Stary A, Wacker SJ, Boukharta L et al. Toward a consensus model of the hERG potassium channel. ChemMedChem 5(3), 455-467 (2010).
90. Moroy G, Martiny VY, Vayer P, Villoutreix BO, Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov. Today 17(1-2), 44-55 (2012).
91. Doyle DA, Cabral JM, Pfuetzner RA et al. The structure of the potassium channel: molecular basis of K+ conduction and selectivity. Science 280(5360), 69-77 (1998).
92. Jiang Y, Lee A, Chen J et al. X-ray structure of a voltagedependent K+ channel. Nature 423(6935), 33-41 (2003).
93. Jiang Y, Lee A, Chen J, Cadene M, Chait BT, MacKinnon R. Crystal structure and mechanism of a calcium-gated potassium channel. Nature 417(6888), 515-522 (2002).
94. Long SB, Campbell EB, MacKinnon R. Crystal structure of a mammalian voltage-dependent shaker family K+ channel. Science 309(5736), 897-903 (2005).
95. Berman HM, Westbrook J, Feng Z et al. The protein databank. Nucleic Acids Res. 8(1), 235-242 (2000).
96. Tristani-Firouzi M, Sanguinetti MC. Structural determinants and biophysical properties of HERG and KCNQ1 channel gating. J. Mol. Cell. Cardiol. 35(1), 27-35 (2003).
97. Yi H, Cao Z, Yin S, Dai C, Wu Y, Li W. Interaction simulation of hERG K+ channel with its specific BeKm-1 peptide: insights into the selectivity of molecular recognition. J. Proteome Res. 6(2), 611-620 (2007).
98. Imai YN, Ryu S, Oiki S. Docking model of drug binding to the human ether-A-go-go potassium channel guided by tandem dimer mutant patch-clamp data: a synergic approach. J. Med. Chem. 52(6), 1630-1638 (2009).
99. Du L, Li M, You Q, Xia L. A novel structure-based virtual screening model for the hERG channel blockers. Biochem. Biophys. Res. Commun. 355(4), 889-894 (2007).
100. Durdagi S, Deshpande S, Duff HJ, Noskov SY. Modeling of open, closed, and open-inactivated states of the hERG1 channel: structural mechanisms of the state-dependent drug binding. J. Chem. Inf. Model. 52(10), 2760-2774 (2012).
101. Dempsey CE, Wright D, Colenso CK, Sessions RB, Hancox JC. Assessing hERG pore models as templates for drug docking using published experimental constraints: the inactivated state in the context of drug block. J. Chem. Inf. Model. 54(2), 601-612 (2014).
102. Schmidtke P, Ciantar M, Theret I, Ducrot P. Dynamics of hERG closure allow novel insights into hERG blocking by small molecules. J. Chem. Inf. Model. 54(8), 2320-2333 (2014).
103. Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: A New Engine for Pharmacophore Perception, 3D QSAR Model Development, and3D Database Screening. 1. Methodology and Preliminary Results. J. Comput. Aided Mol. Des. 20, 647-671 (2006