Machine Learning-Based Modeling of Drug Toxicity

  • Jing Lu
  • Dong Lu
  • Zunyun Fu
  • Mingyue Zheng
  • Xiaomin Luo
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)

Abstract

Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accurate alternatives, such as in silico prediction models. In this review, we discuss the practical use of some prediction models on three toxicity end points, including acute toxicity, carcinogenicity, and inhibition of the human ether-a-go-go-related gene ion channel (hERG). Special emphasis is put on the machine learning methods for developing in silico models, and their advantages and weaknesses are discussed. We conclude that machine learning methods are valuable for helping the process of designing new candidates with low toxicity in drug R&D studies. In the future, much still needs to be done to understand more completely the biological mechanisms for toxicity and to develop more accurate prediction models to screen compounds.

Key words

Machine learning method In silico model Acute toxicity Carcinogenicity hERG 

References

  1. 1.
    CMR International 2010 Global R&D Performance Metrics Programme. http://cmr.thomsonreuters.com/services/programs/randd/
  2. 2.
    Lasser KE, Allen PD, Woolhandler SJ, Himmelstein DU, Wolfe SM, Bor DH (2002) Timing of new black box warnings and withdrawals for prescription medications. JAMA 287(17):2215–2220.  https://doi.org/10.1001/jama.287.17.2215 CrossRefPubMedGoogle Scholar
  3. 3.
    O’Brien SE, de Groot MJ (2005) Greater than the sum of its parts: combining models for useful ADMET prediction. J Med Chem 48(4):1287CrossRefPubMedGoogle Scholar
  4. 4.
    Vanderwall DE, Yuen N, Al-Ansari M, Bailey J, Fram D, Green DV, Pickett S, Vitulli G, Luengo JI, Almenoff JS (2011) Molecular clinical safety intelligence: a system for bridging clinically focused safety knowledge to early-stage drug discovery - the GSK experience. Drug Discov Today 16(15–16):646–653.  https://doi.org/10.1016/j.drudis.2011.05.001. S1359-6446(11)00143-7 [pii]CrossRefPubMedGoogle Scholar
  5. 5.
    Accelrys Toxicity Database 2011.4. Accelrys Software Inc., San Diego, CAGoogle Scholar
  6. 6.
    TOXNET. http://toxnet.nlm.nih.gov/. Accessed 14 Oct 2011
  7. 7.
    SDF Download Page, U.S. EPA. http://www.epa.gov/ncct/dsstox/sdf_isscan_external.html. Accessed 8 July 2012
  8. 8.
    Istituto Superiore di Sanità Website. http://www.iss.it/ampp/dati/cont.php?id=233&lang=1&tipo=7. Accessed 8 July 2012
  9. 9.
    Dobson CM (2004) Chemical space and biology. Nature 432(7019):824–828.  https://doi.org/10.1038/nature03192. nature03192 [pii]CrossRefPubMedGoogle Scholar
  10. 10.
    Walum E (1998) Acute oral toxicity. Environ Health Perspect 106(Suppl 2):497–503CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Label Review Manual (2014) Chapter7: Precautionary statements. U.S. EPA, Washington, DC. https://www.epa.gov/sites/production/files/2015-03/documents/chap-07-jul-2014.pdf. Accessed 20 Apr 2017
  12. 12.
    Li X, Chen L, Cheng F, Wu Z, Bian H, Xu C, Li W, Liu G, Shen X, Tang Y (2014) In silico prediction of chemical acute oral toxicity using multi-classification methods. J Chem Inform Model 54(4):1061–1069CrossRefGoogle Scholar
  13. 13.
    Parasuraman S (2011) Toxicological screening. J Pharmacol Pharmacother 2(2):74–79.  https://doi.org/10.4103/0976-500X.81895. JPP-2-74 [pii]CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Nicolotti O, Benfenati E, Carotti A, Gadaleta D, Gissi A, Mangiatordi GF, Novellino E (2014) REACH and in silico methods: an attractive opportunity for medicinal chemists. Drug Discov Today 19(11):1757–1768.  https://doi.org/10.1016/j.drudis.2014.06.027. S1359-6446(14)00269-4 [pii]CrossRefPubMedGoogle Scholar
  15. 15.
    Benz RD (2007) Toxicological and clinical computational analysis and the US FDA/CDER. Expert Opin Drug Metab Toxicol 3(1):109–124.  https://doi.org/10.1517/17425255.3.1.109 CrossRefPubMedGoogle Scholar
  16. 16.
    Creton S, Dewhurst IC, Earl LK, Gehen SC, Guest RL, Hotchkiss JA, Indans I, Woolhiser MR, Billington R (2010) Acute toxicity testing of chemicals-opportunities to avoid redundant testing and use alternative approaches. Crit Rev Toxicol 40(1):50–83.  https://doi.org/10.3109/10408440903401511 CrossRefPubMedGoogle Scholar
  17. 17.
    Aiken LS, West SG, Pitts SC (2003) Multiple linear regression. In: Handbook of psychology. Wiley, New York.  https://doi.org/10.1002/0471264385.wei0219 Google Scholar
  18. 18.
    Slinker BK, Glantz SA (2008) Multiple linear regression. Accounting for multiple simultaneous determinants of a continuous dependent variable. Circulation 117(13):1732–1737.  https://doi.org/10.1161/circulationaha.106.654376 CrossRefPubMedGoogle Scholar
  19. 19.
    Tranmer M, Elliot M (2008) Multiple linear regression. The Cathie Marsh Centre for Census and Survey Research (CCSR), Oxford, UKGoogle Scholar
  20. 20.
    Helland I (2004) Partial least squares regression. In: Encyclopedia of statistical sciences. Wiley, New York.  https://doi.org/10.1002/0471667196.ess6004.pub2 Google Scholar
  21. 21.
    Geladi P, Kowalski BR (1986) Partial least squares regression: a tutorial. Anal Chim Acta 185:1–17. http://www.udel.edu/chem/analytical/cumes/text-partial%20least-squares%20regression.pdf CrossRefGoogle Scholar
  22. 22.
    Le T, Epa VC, Burden FR, Winkler DA (2012) Quantitative structure-property relationship modeling of diverse materials properties. Chem Rev 112(5):2889–2919.  https://doi.org/10.1021/cr200066h CrossRefPubMedGoogle Scholar
  23. 23.
    Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New YorkGoogle Scholar
  24. 24.
    Toropov AA, Rasulev BF, Leszczynski J (2007) QSAR modeling of acute toxicity for nitrobenzene derivatives towards rats: comparative analysis by MLRA and optimal descriptors. QSAR Comb Sci 26(5):686–693.  https://doi.org/10.1002/qsar.200610135 CrossRefGoogle Scholar
  25. 25.
    Todeschini R, Consonni V (2008) Handbook of molecular descriptors, vol 11. Wiley, New YorkGoogle Scholar
  26. 26.
    Kubinyi H, Folkers G, Martin YC (1998) 3D QSAR in drug design, Ligand-protein interactions and molecular similarity, vol 2. Springer Science & Business Media, Dordrecht, NetherlandsGoogle Scholar
  27. 27.
    Devillers J, Balaban AT (2000) Topological indices and related descriptors in QSAR and QSPAR. CRC Press, Boca RatonGoogle Scholar
  28. 28.
    Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: International 1989 Joint Conference on Neural Networks, 0–0, vol 591, pp 593–605. doi:10.1109/ijcnn.1989.118638Google Scholar
  29. 29.
    Patterson DW (ed) (1998) Artificial neural networks: theory and applications. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  30. 30.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  31. 31.
    Wang L (2005) Support vector machines: theory and applications, vol 177. Springer Science & Business Media, New YorkCrossRefGoogle Scholar
  32. 32.
    Eldred DV, Jurs PC (1999) Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure. SAR QSAR Environ Res 10(2–3):75–99.  https://doi.org/10.1080/10629369908039170 CrossRefPubMedGoogle Scholar
  33. 33.
    Lu J, Lu D, Zhang X, Bi Y, Cheng K, Zheng M, Luo X (2016) Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine. Biochim Biophys Acta 1860(11 Pt B):2664–2671.  https://doi.org/10.1016/j.bbagen.2016.05.019 CrossRefPubMedGoogle Scholar
  34. 34.
    Peng J, Lu J, Shen Q, Zheng M, Luo X, Zhu W, Jiang H, Chen K (2014) In silico site of metabolism prediction for human UGT-catalyzed reactions. Bioinformatics 30(3):398–405.  https://doi.org/10.1093/bioinformatics/btt681 CrossRefPubMedGoogle Scholar
  35. 35.
    Kieslich CA, Smadbeck J, Khoury GA, Floudas CA (2016) conSSert: consensus SVM model for accurate prediction of ordered secondary structure. J Chem Inf Model 56(3):455–461.  https://doi.org/10.1021/acs.jcim.5b00566 CrossRefPubMedGoogle Scholar
  36. 36.
    Wang Y, Zheng M, Xiao J, Lu Y, Wang F, Lu J, Luo X, Zhu W, Jiang H, Chen K (2010) Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow. SAR QSAR Environ Res 21(5–6):559–570.  https://doi.org/10.1080/1062936x.2010.502300 CrossRefPubMedGoogle Scholar
  37. 37.
    Papa E, Villa F, Gramatica P (2005) Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow). J Chem Inf Model 45(5):1256–1266.  https://doi.org/10.1021/ci050212l CrossRefPubMedGoogle Scholar
  38. 38.
    Gini G, Craciun MV, König C, Benfenati E (2004) Combining unsupervised and supervised artificial neural networks to predict aquatic toxicity. J Chem Inf Comput Sci 44(6):1897–1902.  https://doi.org/10.1021/ci0401219 CrossRefPubMedGoogle Scholar
  39. 39.
    Obrezanova O, Csanyi G, Gola JM, Segall MD (2007) Gaussian processes: a method for automatic QSAR modeling of ADME properties. J Chem Inf Model 47(5):1847–1857.  https://doi.org/10.1021/ci7000633 CrossRefPubMedGoogle Scholar
  40. 40.
    Gramacy RB, Apley DW (2015) Local Gaussian process approximation for large computer experiments. J Comput Graph Stat 24(2):561–578.  https://doi.org/10.1080/10618600.2014.914442 CrossRefGoogle Scholar
  41. 41.
    González-Arjona D, López-Pérez G, Gustavo González A (2002) Non-linear QSAR modeling by using multilayer perceptron feedforward neural networks trained by back-propagation. Talanta 56(1):79–90.  https://doi.org/10.1016/S0039-9140(01)00537-9 CrossRefPubMedGoogle Scholar
  42. 42.
    Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232CrossRefGoogle Scholar
  43. 43.
    Lei T, Li Y, Song Y, Li D, Sun H, Hou T (2016) ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J Cheminform 8:6.  https://doi.org/10.1186/s13321-016-0117-7 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244.  https://doi.org/10.1162/15324430152748236 Google Scholar
  45. 45.
    Burden FR, Winkler DA (2015) Relevance vector machines: sparse classification methods for QSAR. J Chem Inf Model 55(8):1529–1534.  https://doi.org/10.1021/acs.jcim.5b00261 CrossRefPubMedGoogle Scholar
  46. 46.
    Larose DT (2005) k-Nearest neighbor algorithm. In: Discovering knowledge in data. Wiley, New York, pp 90–106.  https://doi.org/10.1002/0471687545.ch5 CrossRefGoogle Scholar
  47. 47.
    Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis, vol 8. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  48. 48.
    Johnson MA, Maggiora GM (eds) (1990) Concepts and applications of molecular similarity. Wiley, New YorkGoogle Scholar
  49. 49.
    Breiman L (2001) Random forests. Machine Learning 45(1):5–32. citeulike-article-id:12416445.  https://doi.org/10.1023/a%253a1010933404324 CrossRefGoogle Scholar
  50. 50.
    Romesburg CH (1984) Cluster analysis for researchers. Lifetime Learning publications, Belmont, CAGoogle Scholar
  51. 51.
    Contrera JF, Matthews EJ, Daniel Benz R (2003) Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices. Regul Toxicol Pharmacol 38(3):243–259. S0273230003000710 [pii]CrossRefPubMedGoogle Scholar
  52. 52.
    Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova-Jeliazkova N, Patlewicz GY, Perkins R, Roberts D, Schultz T, Stanton DW, van de Sandt JJ, Tong W, Veith G, Yang C (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern Lab Anim 33(2):155–173PubMedGoogle Scholar
  53. 53.
    Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33(5):445–459PubMedGoogle Scholar
  54. 54.
    Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22(1):69–77.  https://doi.org/10.1002/qsar.200390007 CrossRefGoogle Scholar
  55. 55.
    Lu J, Peng J, Wang J, Shen Q, Bi Y, Gong L, Zheng M, Luo X, Zhu W, Jiang H, Chen K (2014) Estimation of acute oral toxicity in rat using local lazy learning. J Cheminform 6:26.  https://doi.org/10.1186/1758-2946-6-26 CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression, vol 398. Wiley, New YorkCrossRefGoogle Scholar
  57. 57.
    Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier, San FranciscoGoogle Scholar
  58. 58.
    Specht DF (1990) Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification. IEEE Trans Neural Netw 1(1):111–121.  https://doi.org/10.1109/72.80210 CrossRefPubMedGoogle Scholar
  59. 59.
    Xue Y, Li H, Ung CY, Yap CW, Chen YZ (2006) Classification of a diverse set of Tetrahymena pyriformis toxicity chemical compounds from molecular descriptors by statistical learning methods. Chem Res Toxicol 19(8):1030–1039.  https://doi.org/10.1021/tx0600550 CrossRefPubMedGoogle Scholar
  60. 60.
    Watson P (2008) Naïve Bayes classification using 2D Pharmacophore feature triplet vectors. J Chem Inf Model 48(1):166–178.  https://doi.org/10.1021/ci7003253 CrossRefPubMedGoogle Scholar
  61. 61.
    Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. Trans Neur Netw 13(2):415–425.  https://doi.org/10.1109/72.991427 CrossRefGoogle Scholar
  62. 62.
    Chang CC, Lin CJ. LIBSVM -- A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 14 Feb 2013
  63. 63.
    Fei B, Liu J (2006) Binary tree of SVM: a new fast multiclass training and classification algorithm. Trans Neur Netw 17(3):696–704.  https://doi.org/10.1109/tnn.2006.872343 CrossRefGoogle Scholar
  64. 64.
    Cheong S, Sang H, Lee SY (2004) Support vector machines with binary tree architecture for multi-class classification. Neural Inf Process 2:47–51Google Scholar
  65. 65.
    ACD/Labs ToxSuite. Advanced Chemistry Development Inc., Toronto, Canada. Software available at www.acdlabs.com
  66. 66.
    Martin T Toxicity Estimation Software Tool (TEST). Software available at www.epa.gov/nrmrl/std/qsar/qsar.html
  67. 67.
    TOxicity Prediction by Komputer Assisted Technology (TOPKAT). Accelrys Inc., San Diego, USA. Software available at http://accelrys.com
  68. 68.
    ADMET Predictor. Simulations Plus Inc., Lancaster, USA. Software available at www.simulationsplus.com
  69. 69.
    TerraQSAR. TerraBase Inc., Hamilton, Canada. Software available at www.terrabase-inc.com
  70. 70.
    Gonella Diaza R, Manganelli S, Esposito A, Roncaglioni A, Manganaro A, Benfenati E (2015) Comparison of in silico tools for evaluating rat oral acute toxicity. SAR QSAR Environ Res 26(1):1–27.  https://doi.org/10.1080/1062936x.2014.977819 CrossRefGoogle Scholar
  71. 71.
    Drwal MN, Banerjee P, Dunkel M, Wettig MR, Preissner R (2014) ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 42(Web Server issue):W53–W58.  https://doi.org/10.1093/nar/gku401.nar/gku401 CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Muller L, Kikuchi Y, Probst G, Schechtman L, Shimada H, Sofuni T, Tweats D (1999) ICH-harmonised guidances on genotoxicity testing of pharmaceuticals: evolution, reasoning and impact. Mutat Res 436(3):195–225.  https://doi.org/10.1016/s1383-5742(99)00004-6 CrossRefPubMedGoogle Scholar
  73. 73.
    Lagunin AA, Dearden JC, Filimonov DA, Poroikov VV (2005) Computer-aided rodent carcinogenicity prediction. Mutat Res 586(2):138–146.  https://doi.org/10.1016/j.mrgentox.2005.06.005 CrossRefPubMedGoogle Scholar
  74. 74.
    Benigni R (2005) Structure-activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches. Chem Rev 105(5):1767–1800.  https://doi.org/10.1021/cr030049y CrossRefPubMedGoogle Scholar
  75. 75.
    Sato S, Tomita I (2001) Short-term screening method for the prediction of carcinogenicity of chemical substances: current status and problem of an in vivo rodent micronucleus assay. J Health Sci 47(1):1–8.  https://doi.org/10.1248/jhs.47.1 CrossRefGoogle Scholar
  76. 76.
    Benfenati E, Benigni R, Demarini DM, Helma C, Kirkland D, Martin TM, Mazzatorta P, Ouedraogo-Arras G, Richard AM, Schilter B, Schoonen WG, Snyder RD, Yang C (2009) Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 27(2):57–90.  https://doi.org/10.1080/10590500902885593 CrossRefPubMedGoogle Scholar
  77. 77.
    Kruhlak NL, Contrera JF, Benz RD, Matthews EJ (2007) Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products. Adv Drug Deliv Rev 59(1):43–55.  https://doi.org/10.1016/j.addr.2006.10.008 CrossRefPubMedGoogle Scholar
  78. 78.
    Ashby J (1985) Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity. Environ Mutagen 7(6):919–921.  https://doi.org/10.1002/em.2860070613 CrossRefPubMedGoogle Scholar
  79. 79.
    Bailey AB, Chanderbhan R, Collazo-Braier N, Cheeseman MA, Twaroski ML (2005) The use of structure-activity relationship analysis in the food contact notification program. Regul Toxicol Pharmacol 42(2):225–235.  https://doi.org/10.1016/j.yrtph.2005.04.006 CrossRefPubMedGoogle Scholar
  80. 80.
    Munro IC, Ford RA, Kennepohl E, Sprenger JG (1996) Thresholds of toxicological concern based on structure-activity relationships. Drug Metab Rev 28(1–2):209–217.  https://doi.org/10.3109/03602539608994000 CrossRefPubMedGoogle Scholar
  81. 81.
    Kazius J, McGuire R, Bursi R (2005) Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 48(1):312–320.  https://doi.org/10.1021/jm040835a CrossRefPubMedGoogle Scholar
  82. 82.
    Kazius J, Nijssen S, Kok J, Back T, Ijzerman AP (2006) Substructure mining using elaborate chemical representation. J Chem Inf Model 46(2):597–605.  https://doi.org/10.1021/ci0503715 CrossRefPubMedGoogle Scholar
  83. 83.
    Benigni R, Bossa C (2008) Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutat Res 659(3):248–261.  https://doi.org/10.1016/j.mrrev.2008.05.003 CrossRefPubMedGoogle Scholar
  84. 84.
    Wang Y, Lu J, Wang F, Shen Q, Zheng M, Luo X, Zhu W, Jiang H, Chen K (2012) Estimation of carcinogenicity using molecular fragments tree. J Chem Inf Model 52(8):1994–2003.  https://doi.org/10.1021/ci300266p CrossRefPubMedGoogle Scholar
  85. 85.
    Kalgutkar AS, Didiuk MT (2009) Structural alerts, reactive metabolites, and protein covalent binding: how reliable are these attributes as predictors of drug toxicity? Chem Biodivers 6(11):2115–2137CrossRefPubMedGoogle Scholar
  86. 86.
    Benigni R, Bossa C, Tcheremenskaia O (2013) Nongenotoxic carcinogenicity of chemicals: mechanisms of action and early recognition through a new set of structural alerts. Chem Rev 113(5):2940–2957.  https://doi.org/10.1021/cr300206t CrossRefPubMedGoogle Scholar
  87. 87.
    Toivonen H, Srinivasan A, King RD, Kramer S, Helma C (2003) Statistical evaluation of the Predictive Toxicology Challenge 2000–2001. Bioinformatics 19(10):1183–1193.  https://doi.org/10.1093/bioinformatics/btg130 CrossRefPubMedGoogle Scholar
  88. 88.
    Sun HM (2004) Prediction of chemical carcinogenicity from molecular structure. J Chem Inf Comput Sci 44(4):1506–1514.  https://doi.org/10.1021/ci049917y CrossRefPubMedGoogle Scholar
  89. 89.
    Tanabe K, Lucic B, Amic D, Kurita T, Kaihara M, Onodera N, Suzuki T (2010) Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling. Mol Divers 14(4):789–802.  https://doi.org/10.1007/s11030-010-9232-y CrossRefPubMedGoogle Scholar
  90. 90.
    Singh KP, Gupta S, Rai P (2013) Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches. Toxicol Appl Pharmacol 272(2):465–475.  https://doi.org/10.1016/j.taap.2013.06.029 CrossRefPubMedGoogle Scholar
  91. 91.
    Li X, Du Z, Wang J, Wu Z, Li W, Liu G, Shen X, Tang Y (2015) In silico estimation of chemical carcinogenicity with binary and ternary classification methods. Mol Informatics 34(4):228–235.  https://doi.org/10.1002/minf.201400127 CrossRefGoogle Scholar
  92. 92.
    Aronov AM (2005) Predictive in silico modeling for hERG channel blockers. Drug Discov Today 10(2):149–155.  https://doi.org/10.1016/s1359-6446(04)03278-7 CrossRefPubMedGoogle Scholar
  93. 93.
    Taboureau O, Jorgensen FS (2011) 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):375–387CrossRefPubMedGoogle Scholar
  94. 94.
    Cavero I, Mestre M, Guillon JM, Crumb W (2000) Drugs that prolong QT interval as an unwanted effect: assessing their likelihood of inducing hazardous cardiac dysrhythmias. Expert Opin Pharmacother 1(5):947–973.  https://doi.org/10.1517/14656566.1.5.947 CrossRefPubMedGoogle Scholar
  95. 95.
    Elliott DJ, Dondas NY, Munsey TS, Sivaprasadarao A (2009) Movement of the S4 segment in the hERG potassium channel during membrane depolarization. Mol Membr Biol 26(8):435–447.  https://doi.org/10.3109/09687680903321081 CrossRefPubMedGoogle Scholar
  96. 96.
    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(2):568–580.  https://doi.org/10.1002/prot.21400 CrossRefPubMedGoogle Scholar
  97. 97.
    Mitcheson JS, Chen J, Lin M, Culberson C, Sanguinetti MC (2000) A structural basis for drug-induced long QT syndrome. Proc Natl Acad Sci U S A 97(22):12329–12333.  https://doi.org/10.1073/pnas.210244497 CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Wang S, Li Y, Xu L, Li D, Hou T (2013) Recent developments in computational prediction of HERG blockage. Curr Top Med Chem 13(11):1317–1326. CTMC-EPUB-20130509-6 [pii]CrossRefPubMedGoogle Scholar
  99. 99.
    Pearlstein RA, Vaz RJ, Kang J, Chen XL, 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(10):1829–1835CrossRefPubMedGoogle Scholar
  100. 100.
    Wang W, MacKinnon R (2017) Cryo-EM structure of the open human ether-a-go-go-Related K+ Channel hERG. Cell 169(3):422–430 e410. S0092-8674(17)30410-5 [pii].  https://doi.org/10.1016/j.cell.2017.03.048 CrossRefPubMedGoogle Scholar
  101. 101.
    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(2):427–434CrossRefPubMedGoogle Scholar
  102. 102.
    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(18):3844–3853CrossRefPubMedGoogle Scholar
  103. 103.
    Inanobe A, Kamiya N, Murakami S, Fukunishi Y, Nakamura H, Kurachi Y (2008) In silico prediction of the chemical block of human ether-a-go-go-related gene (hERG) K+ current. J Physiol Sci 58(7):459–470.  https://doi.org/10.2170/physiolsci.RV-0114-08-07-R1 CrossRefPubMedGoogle Scholar
  104. 104.
    Aronov AM (2006) Common pharmacophores for uncharged human ether-a-go-go-related gene (hERG) blockers. J Med Chem 49(23):6917–6921.  https://doi.org/10.1021/jm060500o CrossRefPubMedGoogle Scholar
  105. 105.
    Springer C, Sokolnicki KL (2013) A fingerprint pair analysis of hERG inhibition data. Chem Cent J 7(1):167.  https://doi.org/10.1186/1752-153x-7-167 CrossRefPubMedPubMedCentralGoogle Scholar
  106. 106.
    Braga RC, Alves VM, Silva MF, Muratov E, Fourches D, Tropsha A, Andrade CH (2014) Tuning HERG out: antitarget QSAR models for drug development. Curr Top Med Chem 14(11):1399–1415CrossRefPubMedPubMedCentralGoogle Scholar
  107. 107.
    Roche O, Trube G, Zuegge J, Pflimlin P, Alanine A, Schneider G (2002) A virtual screening method for prediction of the HERG potassium channel liability of compound libraries. Chembiochem 3(5):455–459.  https://doi.org/10.1002/1439-7633(20020503)3:5<455::AID-CBIC455>3.0.CO;2-L CrossRefPubMedGoogle Scholar
  108. 108.
    Li Q, Jorgensen FS, Oprea T, Brunak S, Taboureau O (2008) hERG classification model based on a combination of support vector machine method and GRIND descriptors. Mol Pharm 5(1):117–127.  https://doi.org/10.1021/mp700124e CrossRefPubMedGoogle Scholar
  109. 109.
    Wang S, Li Y, Wang J, Chen L, Zhang L, Yu H, Hou T (2012) ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Mol Pharm 9(4):996–1010.  https://doi.org/10.1021/mp300023x CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jing Lu
    • 1
  • Dong Lu
    • 2
    • 3
  • Zunyun Fu
    • 2
  • Mingyue Zheng
    • 2
  • Xiaomin Luo
    • 2
  1. 1.School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of ShandongYantai UniversityYantaiChina
  2. 2.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghaiChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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