ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Molecular Pharmaceutics

Volumn 13, Issue 8, 2016, Pages 285-2866

  Wang, Shuangquan ^a   Sun, Huiyong ^a   Liu, Hui ^a   Li, Dan ^a   Li, Youyong ^c   Hou, Tingjun ^a,b  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c SOOCHOW UNIVERSITY   (China)

Author keywords

ADMET; hERG; machine learning; pharmacophore; recursive partitioning; support vector machine; SVM

Indexed keywords

POTASSIUM CHANNEL BLOCKING AGENT; POTASSIUM CHANNEL HERG;

ANALYTIC METHOD; ARTICLE; BAYESIAN LEARNING; CONTROLLED STUDY; DECISION TREE; MOLECULAR MODEL; PHARMACOPHORE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RECURSIVE PARTITIONING APPROACH; SUPPORT VECTOR MACHINE; BAYES THEOREM; CHEMISTRY; CLUSTER ANALYSIS; DRUG DEVELOPMENT; MACHINE LEARNING;

BAYES THEOREM; CLUSTER ANALYSIS; DRUG DISCOVERY; MACHINE LEARNING; MODELS, MOLECULAR; POTASSIUM CHANNEL BLOCKERS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINE;

EID: 84980016697     PISSN: 15438384     EISSN: 15438392     Source Type: Journal    
DOI: 10.1021/acs.molpharmaceut.6b00471     Document Type: Article

References (53)

1. Smith, P. L.; Baukrowitz, T.; Yellen, G. The inward rectification mechanism of the HERG cardiac potassium channel Nature 1996, 379 (6568) 833-836 10.1038/379833a0
2. Sanguinetti, M. C.; Tristani-Firouzi, M. hERG potassium channels and cardiac arrhythmia Nature 2006, 440 (7083) 463-469 10.1038/nature04710
3. Vandenberg, J. I.; Perry, M. D.; Perrin, M. J.; Mann, S. A.; Ke, Y.; Hill, A. P. hERG K+ channels: structure, function, and clinical significance Physiol. Rev. 2012, 92 (3) 1393-1478 10.1152/physrev.00036.2011
4. Recanatini, M.; Poluzzi, E.; Masetti, M.; Cavalli, A.; De Ponti, F. QT prolongation through hERG K+ channel blockade: current knowledge and strategies for the early prediction during drug development Med. Res. Rev. 2005, 25 (2) 133-166 10.1002/med.20019
5. Villoutreix, B. O.; Taboureau, O. Computational investigations of hERG channel blockers: New insights and current predictive models Adv. Drug Delivery Rev. 2015, 86, 72-82 10.1016/j.addr.2015.03.003
6. Witchel, H. J. The hERG potassium channel as a therapeutic target Expert Opin. Ther. Targets 2007, 11 (3) 321-36 10.1517/14728222.11.3.321
7. Brugada, R.; Hong, K.; Dumaine, R.; Cordeiro, J.; Gaita, F.; Borggrefe, M.; Menendez, T. M.; Brugada, J.; Pollevick, G. D.; Wolpert, C. Sudden death associated with short-QT syndrome linked to mutations in HERG Circulation 2004, 109 (1) 30-35 10.1161/01.CIR.0000109482.92774.3A
8. Curran, M. E.; Splawski, I.; Timothy, K. W.; Vincen, G. M.; Green, E. D.; Keating, M. T. A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome Cell 1995, 80 (5) 795-803 10.1016/0092-8674(95)90358-5
9. Brown, A. Drugs, hERG and sudden death Cell Calcium 2004, 35 (6) 543-547 10.1016/j.ceca.2004.01.008
10. Aronov, A. M. Predictive in silico modeling for hERG channel blockers Drug Discovery Today 2005, 10 (2) 149-155 10.1016/S1359-6446(04)03278-7
11. Raschi, E.; Vasina, V.; Poluzzi, E.; De Ponti, F. The hERG K+ channel: target and antitarget strategies in drug development Pharmacol. Res. 2008, 57 (3) 181-195 10.1016/j.phrs.2008.01.009
12. 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. 2004, 86 (2) 205-233 10.1016/j.pbiomolbio.2003.09.001
13. 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 2002, 3 (5) 455-459 10.1002/1439-7633(20020503)3:5<455::AID-CBIC455>3.0.CO;2-L
14. 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 2012, 9 (8) 2290-2301 10.1021/mp300156r
15. 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. 2011, 46 (2) 618-630 10.1016/j.ejmech.2010.11.042
16. Wang, S.; Li, Y.; Xu, L.; Li, D.; Hou, T. Recent developments in computational prediction of HERG blockage Curr. Top. Med. Chem. 2013, 13 (11) 1317-1326 10.2174/15680266113139990036
17. Jing, Y.; Easter, A.; Peters, D.; Kim, N.; Enyedy, I. J. In silico prediction of hERG inhibition Future Med. Chem. 2015, 7 (5) 571-586 10.4155/fmc.15.18
18. Dempsey, C. E.; Wright, D.; Colenso, C. K.; Sessions, R. B.; Hancox, J. C. 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. 2014, 54 (2) 601-612 10.1021/ci400707h
19. Ekins, S.; Crumb, W. J.; Sarazan, R. D.; Wikel, J. H.; Wrighton, S. A. Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel J. Pharmacol. Exp. Ther. 2002, 301 (2) 427-434 10.1124/jpet.301.2.427
20. Aronov, A. M. Common pharmacophores for uncharged human ether-a-go-go-related gene (hERG) blockers J. Med. Chem. 2006, 49 (23) 6917-6921 10.1021/jm060500o
21. Leong, M. K. A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability Chem. Res. Toxicol. 2007, 20 (2) 217-226 10.1021/tx060230c
22. Garg, D.; Gandhi, T.; Mohan, C. G. Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques J. Mol. Graphics Modell. 2008, 26 (6) 966-976 10.1016/j.jmgm.2007.08.002
23. Durdagi, S.; Duff, H. J.; Noskov, S. Y. 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. 2011, 51 (2) 463-474 10.1021/ci100409y
24. 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. 2012, 18 (3) 1023-1036 10.1007/s00894-011-1136-y
25. Kratz, J. M.; Schuster, D.; Edtbauer, M.; Saxena, P.; Mair, C. E.; Kirchebner, J.; Matuszczak, B.; Baburin, I.; Hering, S.; Rollinger, J. M. Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools J. Chem. Inf. Model. 2014, 54 (10) 2887-901 10.1021/ci5001955
26. Stoll, F.; Goeller, A. H.; Hillisch, A. Utility of protein structures in overcoming ADMET-related issues of drug-like compounds Drug Discovery Today 2011, 16 (11-12) 530-538 10.1016/j.drudis.2011.04.008
27. Chen, L.; Li, Y. Y.; Yu, H. D.; Zhang, L. L.; Hou, T. J. Computational models for predicting substrates or inhibitors of P-glycoprotein Drug Discovery Today 2012, 17 (7-8) 343-351 10.1016/j.drudis.2011.11.003
28. Xu, Y.; Liu, X.; Li, S.; Zhou, N.; Gong, L.; Luo, C.; Luo, X.; Zheng, M.; Jiang, H.; Chen, K. Combinatorial pharmacophore modeling of organic cation transporter 2 (OCT2) inhibitors: insights into multiple inhibitory mechanisms Mol. Pharmaceutics 2013, 10 (12) 4611-4619 10.1021/mp400423g
29. Ding, Y.-L.; Shih, Y.-H.; Tsai, F.-Y.; Leong, M. K. In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2) PLoS One 2014, 9 (3) e90689 10.1371/journal.pone.0090689
30. Xu, Y.; Liu, X.; Wang, Y.; Zhou, N.; Peng, J.; Gong, L.; Ren, J.; Luo, C.; Luo, X.; Jiang, H. Combinatorial Pharmacophore Modeling of Multidrug and Toxin Extrusion Transporter 1 Inhibitors: a Theoretical Perspective for Understanding Multiple Inhibitory Mechanisms Sci. Rep. 2015, 5, 13684 10.1038/srep13684
31. Leong, M. K.; Chen, H.-B.; Shih, Y.-H. Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme PLoS One 2012, 7 (3) e33829 10.1371/journal.pone.0033829
32. Wang, S.; Li, Y.; Wang, J.; Chen, L.; Zhang, L.; Yu, H.; Hou, T. ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage Mol. Pharmaceutics 2012, 9 (4) 996-1010 10.1021/mp300023x
33. Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Zhou, Z.; Han, L.; Karapetyan, K.; Dracheva, S.; Shoemaker, B. A. PubChem's BioAssay database Nucleic Acids Res. 2012, 40 (D1) D400-D412 10.1093/nar/gkr1132
34. Aronov, A. M.; Goldman, B. B. A model for identifying HERG K+ channel blockers Bioorg. Med. Chem. 2004, 12 (9) 2307-2315 10.1016/j.bmc.2004.02.003
35. Li, Q.; Jørgensen, F. S.; Oprea, T.; Brunak, S.; Taboureau, O. hERG classification model based on a combination of support vector machine method and GRIND descriptors Mol. Pharmaceutics 2008, 5 (1) 117-127 10.1021/mp700124e
36. Discovery Studio, version 2.5; Accelrys Inc.: San Diego, CA, 2009.
37. Waring, M. J.; Johnstone, C. A quantitative assessment of hERG liability as a function of lipophilicity Bioorg. Med. Chem. Lett. 2007, 17 (6) 1759-1764 10.1016/j.bmcl.2006.12.061
38. Mannhold, R.; Kubinyi, H.; Folkers, G.; Langer, T.; Hoffmann, R. D. Pharmacophores and pharmacophore searches; John Wiley & Sons: 2006; Vol. 32.
39. Shi, H.; Tian, S.; Li, Y.; Li, D.; Yu, H.; Zhen, X.; Hou, T. Absorption, Distribution, Metabolism, Excretion, and Toxicity Evaluation in Drug Discovery. 14. Prediction of Human Pregnane X Receptor Activators by Using Naive Bayesian Classification Technique Chem. Res. Toxicol. 2015, 28 (1) 116-125 10.1021/tx500389q
40. Chen, L.; Li, Y.; Zhao, Q.; Peng, H.; Hou, T. ADME evaluation in drug discovery. 10. Predictions of P-glycoprotein inhibitors using recursive partitioning and naive Bayesian classification techniques Mol. Pharmaceutics 2011, 8 (3) 889-900 10.1021/mp100465q
41. Tian, S.; Wang, J.; Li, Y.; Xu, X.; Hou, T. Drug-likeness analysis of traditional Chinese medicines: prediction of drug-likeness using machine learning approaches Mol. Pharmaceutics 2012, 9 (10) 2875-2886 10.1021/mp300198d
42. Hou, T.; Zhang, W.; Wang, J.; Wang, W. Predicting drug resistance of the HIV-1 protease using molecular interaction energy components Proteins: Struct., Funct., Genet. 2009, 74 (4) 837-846 10.1002/prot.22192
43. Hou, T.; Zhang, W.; Case, D. A.; Wang, W. Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain J. Mol. Biol. 2008, 376 (4) 1201-1214 10.1016/j.jmb.2007.12.054
44. Hou, T.; Wang, J.; Li, Y. ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine J. Chem. Inf. Model. 2007, 47 (6) 2408-2415 10.1021/ci7002076
45. Hou, T.; Li, N.; Li, Y.; Wang, W. Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models J. Proteome Res. 2012, 11 (5) 2982-2995 10.1021/pr3000688
46. Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines ACM T. Intel. Syst. Tec. 2011, 10.1145/1961189.1961199
47. Meyer, D. Support vector machines: The interface to libsvm in package e1071. 2004.
48. Liu, L.-l.; Lu, J.; Lu, Y.; Zheng, M.-y.; Luo, X.-m.; Zhu, W.-l.; Jiang, H.-l.; Chen, K.-x. Novel Bayesian classification models for predicting compounds blocking hERG potassium channels Acta Pharmacol. Sin. 2014, 35 (8) 1093-1102 10.1038/aps.2014.35
49. Hou, T.; Wang, J.; Zhang, W.; Wang, W.; Xu, X. Recent advances in computational prediction of drug absorption and permeability in drug discovery Curr. Med. Chem. 2006, 13 (22) 2653-2667 10.2174/092986706778201558
50. Hou, T.; Wang, J. Structure-ADME relationship: still a long way to go? Expert Opin. Drug Metab. Toxicol. 2008, 4 (6) 759-770 10.1517/17425255.4.6.759
51. Du, L.; Li, M.; You, Q. The interactions between hERG potassium channel and blockers Curr. Top. Med. Chem. 2009, 9 (4) 330-338 10.2174/156802609788317829
52. Wei, Q.; Dunbrack, R. L., Jr The role of balanced training and testing data sets for binary classifiers in bioinformatics PLoS One 2013, 8 (7) e67863 10.1371/journal.pone.0067863
53. Sun, H. An accurate and interpretable Bayesian classification model for prediction of hERG liability ChemMedChem 2006, 1 (3) 315-322 10.1002/cmdc.200500047