Skip to main content
SpringerLink
Log in

Compilation and physicochemical classification analysis of a diverse hERG inhibition database

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

A large and chemically diverse hERG inhibition data set comprised of 6690 compounds was constructed on the basis of ChEMBL bioactivity database and original publications dealing with experimental determination of hERG activities using patch-clamp and competitive displacement assays. The collected data were converted to binary format at 10 µM activity threshold and subjected to gradient boosting machine classification analysis using a minimal set of physicochemical and topological descriptors. The tested parameters involved lipophilicity (log P), ionization (pK a ), polar surface area, aromaticity, molecular size and flexibility. The employed approach allowed classifying the compounds with an overall 75–80 % accuracy, even though it only accounted for non-specific interactions between hERG and ligand molecules. The observed descriptor-response profiles were consistent with common knowledge about hERG ligand binding site, but also revealed several important quantitative trends, as well as slight inter-assay variability in hERG inhibition data. The results suggest that even weakly basic groups (pK a  < 6) might substantially contribute to hERG inhibition potential, whereas the role of lipophilicity depends on the compound’s ionization state, and the influence of log P decreases in the order of bases > zwitterions > neutrals > acids. Given its robust performance and clear physicochemical interpretation, the proposed model may provide valuable information to direct drug discovery efforts towards compounds with reduced risk of hERG-related cardiotoxicity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Vandenberg JI, Perry MD, Perrin MJ, Mann SA, Ke Y, Hill AP (2012) hERG K(+) channels: structure, function, and clinical significance. Physiol Rev 92:1393–1478

    Article  CAS  Google Scholar 

  2. Sanguinetti MC, Tristani-Firouzi M (2006) hERG potassium channels and cardiac arrhythmia. Nature 440:463–469

    Article  CAS  Google Scholar 

  3. Redfern WS, Carlsson L, Davis AS, Lynch WG, MacKenzie I, Palethorpe S, Siegl PKS, Strang I, Sullivan AT, Wallis R, Camm AJ, Hammond TG (2003) 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 58:32–45

    Article  CAS  Google Scholar 

  4. Fermini B, Fossa AA (2003) The impact of drug-induced QT interval prolongation on drug discovery and development. Nat Rev Drug Discov 2:439–447

    Article  CAS  Google Scholar 

  5. Mitcheson JS, Chen J, Lin M, Culberson C, Sanguinetti MC (2000) A structural basis for drug-induced long QT syndrome. Proc Natl Acad Sci USA 97:12329–12333

    Article  CAS  Google Scholar 

  6. Sanguinetti MC, Mitcheson JS (2005) Predicting drug–hERG channel interactions that cause acquired long QT syndrome. Trends Pharmacol Sci 26:119–124

    Article  CAS  Google Scholar 

  7. Fernandez D, Ghanta A, Kauffman GW, Sanguinetti MC (2004) Physicochemical features of the HERG channel drug binding site. J Biol Chem 279:10120–10127

    Article  CAS  Google Scholar 

  8. Mitcheson JS (2008) hERG potassium channels and the structural basis of drug-induced arrhythmias. Chem Res Toxicol 21:1005–1010

    Article  CAS  Google Scholar 

  9. Wallis RM (2010) Integrated risk assessment and predictive value to humans of non-clinical repolarization assays. Br J Pharmacol 159:115–121

    Article  CAS  Google Scholar 

  10. Witchel HJ (2011) Drug-induced hERG block and long QT syndrome. Cardiovasc Ther 29:251–259

    Article  CAS  Google Scholar 

  11. Jamieson C, Moir EM, Rankovic Z, Wishart G (2006) Medicinal chemistry of hERG optimizations: highlights and hang-ups. J Med Chem 49:5029–5046

    Article  CAS  Google Scholar 

  12. Waring MJ, Johnstone C (2007) A quantitative assessment of hERG liability as a function of lipophilicity. Bioorg Med Chem Lett 17:1759–1764

    Article  CAS  Google Scholar 

  13. Gleeson MP (2008) Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem 51:817–834

    Article  CAS  Google Scholar 

  14. Gleeson P, Bravi G, Modi S, Lowe D (2009) ADMET rules of thumb II: a comparison of the effects of common substituents on a range of ADMET parameters. Bioorg Med Chem 17:5906–5919

    Article  CAS  Google Scholar 

  15. Osterberg F, Aqvist J (2005) Exploring blocker binding to a homology model of the open hERG K+ channel using docking and molecular dynamics methods. FEBS Lett 579:2939–2944

    Article  Google Scholar 

  16. Stansfeld PJ, Gedeck P, Gosling M, Cox B, Mitcheson JS, Sutcliffe MJ (2007) Drug block of the hERG potassium channel: insight from modeling. Proteins 68:568–580

    Article  CAS  Google Scholar 

  17. Masetti M, Cavalli A, Recanatini M (2008) Modeling the hERG potassium channel in a phospholipid bilayer: molecular dynamics and drug docking studies. J Comput Chem 29:795–808

    Article  CAS  Google Scholar 

  18. Dempsey CE, Wright D, Colenso CK, Sessions RB, Hancox JC (2014) 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 54:601–612

    Article  CAS  Google Scholar 

  19. Cavalli A, Poluzzi E, De Ponti F, Recanatini M (2002) Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K(+) channel blockers. J Med Chem 45:3844–3853

    Article  CAS  Google Scholar 

  20. Ekins S, Crumb WJ, Sarazan RD, Wikel JH, Wrighton SA (2002) Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel. J Pharmacol Exp Ther 301:427–434

    Article  CAS  Google Scholar 

  21. Pearlstein RA, Vaz RJ, Kang J, Chen X-L, Preobrazhenskaya M, Shchekotikhin AE, Korolev AM, Lysenkova LN, Miroshnikova OV, Hendrix J, Rampe D (2003) Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches. Bioorg Med Chem Lett 13:1829–1835

    Article  CAS  Google Scholar 

  22. Aronov AM, Goldman BB (2004) A model for identifying HERG K+ channel blockers. Bioorg Med Chem 12:2307–2315

    Article  CAS  Google Scholar 

  23. Aronov AM (2006) Common pharmacophores for uncharged human ether-a-go-go-related gene (hERG) blockers. J Med Chem 49:6917–6921

    Article  CAS  Google Scholar 

  24. Cavalli A, Buonfiglio R, Ianni C, Masetti M, Ceccarini L, Caves R, Chang MWY, Mitcheson JS, Roberti M, Recanatini M (2012) Computational design and discovery of “minimally structured” hERG blockers. J Med Chem 55:4010–4014

    Article  CAS  Google Scholar 

  25. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Krüger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–D1090

    Article  CAS  Google Scholar 

  26. Wang S, Li Y, Xu L, Li D, Hou T (2013) Recent developments in computational prediction of HERG blockage. Curr Top Med Chem 13:1317–1326

    Article  CAS  Google Scholar 

  27. Braga RC, Alves VM, Silva MFB, Muratov E, Fourches D, Tropsha A, Andrade CH (2014) Tuning HERG out: antitarget QSAR models for drug development. Curr Top Med Chem 14:1399–1415

    Article  CAS  Google Scholar 

  28. Villoutreix BO, Taboureau O (2015) Computational investigations of hERG channel blockers: new insights and current predictive models. Adv Drug Deliv Rev 86:72–82

    Article  CAS  Google Scholar 

  29. Gavaghan CL, Arnby CH, Blomberg N, Strandlund G, Boyer S (2007) Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. J Comput Aided Mol Des 21:189–206

    Article  CAS  Google Scholar 

  30. Schyman P, Liu R, Wallqvist A (2016) General purpose 2D and 3D similarity approach to identify hERG blockers. J Chem Inf Model 56:213–222

    Article  CAS  Google Scholar 

  31. Saxena P, Zangerl-Plessl E-M, Linder T, Windisch A, Hohaus A, Timin E, Hering S, Stary-Weinzinger A (2016) New potential binding determinant for hERG channel inhibitors. Sci Rep. doi:10.1038/srep24182

    Google Scholar 

  32. Du F, Babcock JJ, Yu H, Zou B, Li M (2015) Global analysis reveals families of chemical motifs enriched for HERG inhibitors. PLoS ONE. doi:10.1371/journal.pone.0118324

    Google Scholar 

  33. Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S, Owen RM, Pairaudeau G, Pennie WD, Pickett SD, Wang J, Wallace O, Weir A (2015) An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov 14:475–486

    Article  CAS  Google Scholar 

  34. Aronov AM (2005) Predictive in silico modeling for hERG channel blockers. Drug Discov Today 10:149–155

    Article  CAS  Google Scholar 

  35. Polak S, Wiśniowska B, Brandys J (2009) Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs’ cardiotoxic properties. J Appl Toxicol 29:183–206

    Article  CAS  Google Scholar 

  36. Doddareddy MR, Klaasse EC, Shagufta Ijzerman AP, Bender A (2010) Prospective validation of a comprehensive in silico hERG model and its applications to commercial compound and drug databases. ChemMedChem 5:716–729

    Article  CAS  Google Scholar 

  37. Broccatelli F, Mannhold R, Moriconi A, Giuli S, Carosati E (2012) QSAR modeling and data mining link Torsades de Pointes risk to the interplay of extent of metabolism, active transport, and HERG liability. Mol Pharm 9:2290–2301

    Article  CAS  Google Scholar 

  38. Ly JQ, Shyy G, Misner DL (2007) Assessing hERG channel inhibition using PatchXpress. Clin Lab Med 27:201–208

    Article  Google Scholar 

  39. Elkins RC, Davies MR, Brough SJ, Gavaghan DJ, Cui Y, Abi-Gerges N, Mirams GR (2013) Variability in high-throughput ion-channel screening data and consequences for cardiac safety assessment. J Pharmacol Toxicol Methods 68:112–122

    Article  CAS  Google Scholar 

  40. Danker T, Möller C (2014) Early identification of hERG liability in drug discovery programs by automated patch clamp. Front Pharmacol. doi:10.3389/fphar.2014.00203

    Google Scholar 

  41. Priest BT, Bell IM, Garcia ML (2008) Role of hERG potassium channel assays in drug development. Channels (Austin) 2:87–93

    Article  Google Scholar 

  42. Murphy SM, Palmer M, Poole MF, Padegimas L, Hunady K, Danzig J, Gill S, Gill R, Ting A, Sherf B, Brunden K, Stricker-Krongrad A (2006) Evaluation of functional and binding assays in cells expressing either recombinant or endogenous hERG channel. J Pharmacol Toxicol Methods 54:42–55

    Article  CAS  Google Scholar 

  43. Chiu PJS, Marcoe KF, Bounds SE, Lin C-H, Feng J-J, Lin A, Cheng F-C, Crumb WJ, Mitchell R (2004) Validation of a [3H]astemizole binding assay in HEK293 cells expressing HERG K+ channels. J Pharmacol Sci 95:311–319

    Article  CAS  Google Scholar 

  44. Raab CE, Butcher JW, Connolly TM, Karczewski J, Yu NX, Staskiewicz SJ, Liverton N, Dean DC, Melillo DG (2006) Synthesis of the first sulfur-35-labeled hERG radioligand. Bioorg Med Chem Lett 16:1692–1695

    Article  CAS  Google Scholar 

  45. Cheng Y, Prusoff WH (1973) Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem Pharmacol 22:3099–3108

    Article  CAS  Google Scholar 

  46. Diaz GJ, Daniell K, Leitza ST, Martin RL, Su Z, McDermott JS, Cox BF, Gintant GA (2004) The [3H]dofetilide binding assay is a predictive screening tool for hERG blockade and proarrhythmia: comparison of intact cell and membrane preparations and effects of altering [K+]o. J Pharmacol Toxicol Methods 50:187–199

    Article  CAS  Google Scholar 

  47. Ertl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area as a sum of fragment-based contribution and its application to the prediction of drug transport properties. J Med Chem 43:3714–3717

    Article  CAS  Google Scholar 

  48. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623

    Article  CAS  Google Scholar 

  49. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232

    Article  Google Scholar 

  50. Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot. doi:10.3389/fnbot.2013.00021

    Google Scholar 

  51. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813

    Article  CAS  Google Scholar 

  52. Cortes-Ciriano I, Bender A, Malliavin TE (2015) Comparing the influence of simulated experimental errors on 12 machine learning algorithms in bioactivity modeling using 12 diverse data sets. J Chem Inf Model 55:1413–1425

    Article  CAS  Google Scholar 

  53. Powers D (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2:37–63

    Google Scholar 

  54. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874

    Article  Google Scholar 

  55. Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37:360–363

    Google Scholar 

  56. Czodrowski P (2014) Count on kappa. J Comput Aided Mol Des 28:1049–1055

    Article  CAS  Google Scholar 

  57. Japertas P, Didziapetris R, Petrauskas A (2002) Fragmental methods in the design of new compounds. applications of the advanced algorithm builder. Quant Struct-Act Relat 21:23–37

    Article  CAS  Google Scholar 

  58. ACD/Percepta (2015) ACD/Labs, Inc., Toronto, Ontario, Canada. http://www.acdlabs.com/products/percepta/

  59. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

  60. Ridgeway G (2015) gbm: generalized boosted regression models. R package version 2.1.1

  61. Hijmans RJ, Phillips S, Leathwick J, Elith J (2016) dismo: species distribution modeling. R package version 1.1-1

  62. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. doi:10.1186/1471-2105-12-77

    Google Scholar 

  63. O’Brien SE, de Groot MJ (2005) Greater than the sum of its parts: combining models for useful ADMET prediction. J Med Chem 48:1287–1291

    Article  Google Scholar 

  64. Vilums M, Overman J, Klaasse E, Scheel O, Brussee J, IJzerman AP (2012) Understanding of molecular substructures that contribute to hERG K+ channel blockade: synthesis and biological evaluation of E-4031 analogues. ChemMedChem 7:107–113

    Article  CAS  Google Scholar 

  65. Zhu B-Y, Jia ZJ, Zhang P, Su T, Huang W, Goldman E, Tumas D, Kadambi V, Eddy P, Sinha U, Scarborough RM, Song Y (2006) Inhibitory effect of carboxylic acid group on hERG binding. Bioorg Med Chem Lett 16:5507–5512

    Article  CAS  Google Scholar 

  66. Czodrowski P (2013) hERG me out. J Chem Inf Model 53:2240–2251

    Article  CAS  Google Scholar 

  67. Liu L, Lu J, Lu Y, Zheng M, Luo X, Zhu W, Jiang H, Chen K (2014) Novel Bayesian classification models for predicting compounds blocking hERG potassium channels. Acta Pharmacol Sin 35:1093–1102

    Article  CAS  Google Scholar 

  68. Yu H, Zou B, Wang X, Li M (2016) Investigation of miscellaneous hERG inhibition in large diverse compound collection using automated patch-clamp assay. Acta Pharmacol Sin 37:111–123

    Article  CAS  Google Scholar 

  69. Berglund S, Egner BJ, Gradén H, Gradén J, Morgan DGA, Inghardt T, Giordanetto F (2009) Optimization of piperidin-4-yl-urea-containing melanin-concentrating hormone receptor 1 (MCH-R1) antagonists: reducing hERG-associated liabilities. Bioorg Med Chem Lett 19:4274–4279

    Article  CAS  Google Scholar 

  70. Ellis JM, Altman MD, Bass A, Butcher JW, Byford AJ, Donofrio A, Galloway S, Haidle AM, Jewell J, Kelly N, Leccese EK, Lee S, Maddess M, Miller JR, Moy LY, Osimboni E, Otte RD, Reddy MV, Spencer K, Sun B, Vincent SH, Ward GJ, Woo GHC, Yang C, Houshyar H, Northrup AB (2015) Overcoming mutagenicity and ion channel activity: optimization of selective spleen tyrosine kinase inhibitors. J Med Chem 58:1929–1939

    Article  CAS  Google Scholar 

  71. Papadatos G, Alkarouri M, Gillet VJ, Willett P, Kadirkamanathan V, Luscombe CN, Bravi G, Richmond NJ, Pickett SD, Hussain J, Pritchard JM, Cooper AWJ, Macdonald SJF (2010) Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity. J Chem Inf Model 50:1872–1886

    Article  CAS  Google Scholar 

  72. Twiner MJ, Doucette GJ, Rasky A, Huang X-P, Roth BL, Sanguinetti MC (2012) Marine algal toxin azaspiracid is an open-state blocker of hERG potassium channels. Chem Res Toxicol 25:1975–1984

    Article  CAS  Google Scholar 

  73. Singleton DH, Boyd H, Steidl-Nichols JV, Deacon M, de Groot MJ, Price D, Nettleton DO, Wallace NK, Troutman MD, Williams C, Boyd JG (2007) Fluorescently labeled analogues of dofetilide as high-affinity fluorescence polarization ligands for the human ether-a-go-go-related gene (hERG) channel. J Med Chem 50:2931–2941

    Article  CAS  Google Scholar 

  74. Louvel J, Carvalho JFS, Yu Z, Soethoudt M, Lenselink EB, Klaasse E, Brussee J, Ijzerman AP (2013) Removal of human ether-à-go-go related gene (hERG) K + channel affinity through rigidity: a case of clofilium analogues. J Med Chem 56:9427–9440

    Article  CAS  Google Scholar 

  75. Sorota S, Zhang X-S, Margulis M, Tucker K, Priestley T (2005) Characterization of a hERG screen using the IonWorks HT: comparison to a hERG rubidium efflux screen. Assay Drug Dev Technol 3:47–57

    Article  CAS  Google Scholar 

  76. Bridgland-Taylor MH, Hargreaves AC, Easter A, Orme A, Henthorn DC, Ding M, Davis AM, Small BG, Heapy CG, Abi-Gerges N, Persson F, Jacobson I, Sullivan M, Albertson N, Hammond TG, Sullivan E, Valentin J-P, Pollard CE (2006) Optimisation and validation of a medium-throughput electrophysiology-based hERG assay using IonWorks HT. J Pharmacol Toxicol Methods 54:189–199

    Article  CAS  Google Scholar 

  77. Gillie DJ, Novick SJ, Donovan BT, Payne LA, Townsend C (2013) Development of a high-throughput electrophysiological assay for the human ether-à-go-go related potassium channel hERG. J Pharmacol Toxicol Methods 67:33–44

    Article  CAS  Google Scholar 

  78. Kramer C, Fuchs JE, Whitebread S, Gedeck P, Liedl KR (2014) Matched molecular pair analysis: significance and the impact of experimental uncertainty. J Med Chem 57:3786–3802

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VšĮ „Aukštieji algoritmai“, A.Mickevičiaus 29, 08117, Vilnius, Lithuania

    Remigijus Didziapetris & Kiril Lanevskij

  2. ACD/Labs, Inc., 8 King Street East, Suite 107, Toronto, ON, M5C 1B5, Canada

    Remigijus Didziapetris & Kiril Lanevskij

Authors
  1. Remigijus Didziapetris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kiril Lanevskij
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kiril Lanevskij.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 22 kb)

Supplementary material 2 (XLSX 1172 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Didziapetris, R., Lanevskij, K. Compilation and physicochemical classification analysis of a diverse hERG inhibition database. J Comput Aided Mol Des 30, 1175–1188 (2016). https://doi.org/10.1007/s10822-016-9986-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-016-9986-0

Keywords

Search

Navigation