Using online tool (iPrior) for modeling toxcast™ assays towards prioritization of animal toxicity testing

Combinatorial Chemistry and High Throughput Screening

Volumn 18, Issue 4, 2015, Pages 420-438

  Abdelaziz, Ahmed ^a   Sushko, Yurii ^a   Novotarskyi, Sergii ^a   Körner, Robert ^a   Brandmaier, Stefan ^b   Tetko, Igor V ^a,b  

^a EADMET GmbH   (Germany)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

Author keywords

Alternative testing; Computational toxicology; iPRIOR; QSAR; REACH; ToxCast

Indexed keywords

CELL NUCLEUS RECEPTOR; CHEMICAL COMPOUND;

ACCURACY; ARTICLE; BIOASSAY; CHEMICAL STRUCTURE; COMPUTER MODEL; COMPUTER PROGRAM; DATA BASE; EXPERIMENTAL ANIMAL; IN VITRO STUDY; IN VIVO STUDY; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL MODEL; NONHUMAN; ONLINE SYSTEM; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; TOXICITY TESTING; ANIMAL; INTERNET;

ANIMALIA;

ANIMALS; INTERNET; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; TOXICITY TESTS;

EID: 84931260682     PISSN: 13862073     EISSN: 18755402     Source Type: Journal    
DOI: 10.2174/1386207318666150305155255     Document Type: Article

References (80)

1. REACH-Registration, Evaluation, Authorisation and Restriction of Chemicals http://ec.europa.eu/enterprise/sectors/chemicals/reach/index-en.htm (accessed Sep 8, 2014).
2. Mission-ECHA http://echa.europa.eu/web/guest/about-us/whowe-are/mission (accessed Jan 1, 2015).
3. REACH-Chemicals-Environment-European Commission http://ec.europa.eu/environment/chemicals/reach/reach-en.htm (accessed Sep 8, 2014).
4. Judson, R.S.; Houck, K.A.; Kavlock, R.J.; Knudsen, T.B.; Martin, M.T.; Mortensen, H.M.; Reif, D.M.; Rotroff, D.M.; Shah, I.; Richard, A.M.; Dix, D.J. In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project. Environ. Health Perspect., 2010, 118, 485-492.
5. Dix, D.J.; Houck, K.A.; Martin, M.T.; Richard, A.M.; Setzer, R.W.; Kavlock, R.J. The ToxCast Program for Prioritizing Toxicity Testing of Environmental Chemicals. Toxicol. Sci., 2007, 95, 5-12.
6. Toxicology, U.E.-N.C. for C. ToxCast http://epa.gov/ncct/toxcast/data.html (accessed Sep 3, 2014).
7. Tice, R.; Kavlock, R.; Christopher Austin. The U.S. "Tox21 Community" and the Future of Toxicology http://www.epa.gov/ncct/bosc-review/2009/posters/1-08-Tice-CompTox-BOSC09.pdf (accessed Jan 15, 2014).
8. Betts, K.S. Tox21 to Date: Steps toward Modernizing Human Hazard Characterization. Environ. Health Perspect., 2013, 121, A228.
9. Toxicology, U.E.-N.C. for C. Tox21 http://www.epa.gov/ncct/Tox 21/(accessed Jan 1, 2015).
10. EPA. Overview of National Research Council Toxicity Testing Strategy http://www.epa.gov/pesticides/science/nrc-toxtesting.html (accessed Sep 8, 2014).
11. EPA. Framework for an EPA Chemical Safety for Sustainability Research Program; 2011; p. 25.
12. Robert J. Kavlock Keith A. Houck, Richard S. Judson, D.J.D.; Richard, M.T.M. and A.M. ToxCast: Developing Predictive Signatures for Chemical Toxicity. In 6th World Congress on Alternatives & Animal Use in the Life Sciences; Japanese Society for Alternatives to Animal Experiments (JSAAE): Tokyo, Japan, 2007.
13. Martin, M.T.; Knudsen, T.B.; Reif, D.M.; Houck, K.A.; Judson, R.S.; Kavlock, R.J.; Dix, D.J. Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening. Biol. Reprod., 2011, 85, 327-339.
14. Shah, I.; Houck, K.; Judson, R.S.; Kavlock, R.J.; Martin, M.T.; Reif, D.M.; Wambaugh, J.; Dix, D.J. Using Nuclear Receptor Activity to Stratify Hepatocarcinogens. PLoS One, 2011, 6, e14584.
15. Kleinstreuer, N.C.; Judson, R.S.; Reif, D.M.; Sipes, N.S.; Singh, A. V; Chandler, K.J.; Dewoskin, R.; Dix, D.J.; Kavlock, R.J.; Knudsen, T.B. Environmental Impact on Vascular Development Predicted by High-Throughput Screening. Environ. Health Perspect., 2011, 119, 1596-1603.
16. Thomas, R.S.; Black, M.B.; Li, L.; Healy, E.; Chu, T.-M.; Bao, W.; Andersen, M.E.; Wolfinger, R.D. A Comprehensive Statistical Analysis of Predicting in Vivo Hazard Using High-Throughput in Vitro Screening. Toxicol. Sci., 2012, 128, 398-417.
17. iPrior-Prioritization and estimation of toxicity of chemcial compounds http://iprior.ochem.eu/home/show.do (accessed Jan 9, 2015).
18. Gene Ontology Documentation http://www.geneontology.org/GO. contents.doc.shtml (accessed Jan 9, 2015).
19. Pathway Commons http://www.pathwaycommons.org/about/(accessed Jan 9, 2015).
20. Ingenuity IPA-Integrate and understand complex 'omics data http://www.ingenuity.com/products/ipa (accessed Jan 9, 2015).
21. KEGG: Kyoto Encyclopedia of Genes and Genomes http://www. genome.jp/kegg/(accessed Jan 9, 2015).
22. Boyadjiev, S.A.; Jabs, E.W. Online Mendelian Inheritance in Man (OMIM) as a Knowledgebase for Human Developmental Disorders. Clin. Genet., 2000, 57, 253-266.
23. US EPA, O. of W.C. and O. of E.I. ToxRefDB (Toxicity Reference Database).
24. Martin, M.T.; Houck, K.A.; McLaurin, K.; Richard, A.M.; Dix, D.J. Linking Regulatory Toxicological Information on Environmental Chemicals with High-Throughput Screening (HTS) and Genomic Data. Toxicol. CD-An Off. J. Soc. Toxicol., 2007, 96, 219-220.
25. Martin, M.T.; Judson, R.S.; Reif, D.M.; Kavlock, R.J.; Dix, D.J. Profiling Chemicals Based on Chronic Toxicity Results from the US EPA ToxRef Database. Environ. Health Perspect., 2009, 117, 392.
26. Sushko, I.; Novotarskyi, S.; Korner, R.; Pandey, A.K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V. V; Tanchuk, V.Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin II; Palyulin, V.A.; Radchenko, E. V; Welsh, W.J.; Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-de-Sousa, J.; Zhang, Q.Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I. V. Online Chemical Modeling Environment (OCHEM): Web Platform for Data Storage, Model Development and Publishing of Chemical Information. J. Comput. Aided. Mol. Des., 2011, 25, 533-554.
27. Brandmaier, S.; Peijnenburg, W.; Durjava, M.K.; Kolar, B.; Gramatica, P.; Papa, E.; Bhhatarai, B.; Kovarich, S.; Cassani, S.; Roy, P.P.; Rahmberg, M.; Öberg, T.; Jeliazkova, N.; Golsteijn, L.; Comber, M.; Charochkina, L.; Novotarskyi, S.; Sushko, I.; Abdelaziz, A.; D'Onofrio, E.; Kunwar, P.; Ruggiu, F.; Tetko, I. V. The QSPR-THESAURUS: The Online Platform of the CADASTER Project. Altern. Lab. Anim., 2014, 42, 13-24.
28. Guide, K.Q.; Screen, W.; Status, N.; Flow, E.; Nodes, A.; Nodes, C.; Nodes, C.; Nodes, E.; Voyage, Y.O.; Workbench, K.; Guide, U.; Views, A.; Projects, W.; Nodes, F.; Repository, N.; Description, N.; Gui, K.; Key, M.; Editor, W.; Options, N.; All, E.; View, O.; View, O.O.; All, C.; Custom, E.; Name, N.; Knime, C.; Page, G. KNIME Quickstart Guide. 1-27.
29. Using SOAP web-services-OCHEM user's manual-eADMET docs http://docs.ochem.eu/display/MAN/Using+SOAP+webservices (accessed Jan 5, 2015).
30. Tetko, I. V; Sushko, I.; Pandey, A.K.; Zhu, H.; Tropsha, A.; Papa, E.; Oberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena Pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection. J. Chem. Inf. Model., 2008, 48, 1733-1746.
31. Tetko, I. V; Bruneau, P.; Mewes, H.-W.; Rohrer, D.C.; Poda, G.I. Can We Estimate the Accuracy of ADME-Tox Predictions? Drug Discov. Today, 2006, 11, 700-707.
32. Sadowski, J.; Gasteiger, J.; Klebe, G. Comparison of Automatic Three-Dimensional Model Builders Using 639 X-Ray Structures. J. Chem. Inf. Comput. Sci., 1994, 34, 1000-1008.
33. Beyer, K.; Goldstein, J.; Ramakrishnan, R.; Shaft, U. When Is "nearest Neighbor" Meaningful? In Database Theory-ICDT'99; Springer, 1999; pp. 217-235.
34. Aha, D.W.; Kibler, D.; Albert, M.K. Instance-Based Learning Algorithms. Mach. Learn., 1991, 6, 37-66.
35. Tetko, I. V. Associative Neural Network. Neural Process. Lett., 2002, 16, 187-199.
36. Tetko, I. V. Neural Network Studies. 4. Introduction to Associative Neural Networks. J. Chem. Inf. Comput. Sci., 2002, 42, 717-728.
37. Rosenblatt, F. The Perceptron, a Perceiving and Recognizing Automaton Project Para; Cornell Aeronautical Laboratory, 1957.
38. Tollenaere, T. SuperSAB: Fast Adaptive Back Propagation with Good Scaling Properties. Neural Networks, 1990, 3, 561-573.
39. Quinlan, J.R. C4. 5: Programs for Machine Learning; Morgan kaufmann, 1993; Vol. 1.
40. Holmes, G.; Donkin, A.; Witten, I.H. WEKA: A Machine Learning Workbench. In; 1994; pp. 357-361.
41. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA Data Mining Software: An Update. ACM SIGKDD Explor. Newsl., 2009, 11, 10-18.
42. Wold, S.; Geladi, P.; Esbensen, K.; Öhman, J. Multi-Way Principal Components-and PLS-Analysis. J. Chemom., 1987, 1, 41-56.
43. Eriksson, L.; Johansson, E. Multivariate Design and Modeling in QSAR. Chemom. Intell. Lab. Syst., 1996, 34, 1-19.
44. Zhokhova, N.I.; Baskin, I.I.; Palyulin, V.A.; Zefirov, A.N.; Zefirov, N.S. Fragmental Descriptors with Labeled Atoms and Their Application in QSAR/QSPR Studies. In Doklady Chemistry; 2007; Vol. 417, pp. 282-284.
45. Wold, S.; Sjöström, M.; Eriksson, L. PLS-Regression: A Basic Tool of Chemometrics. Chemom. Intell. Lab. Syst., 2001, 58, 109-130.
46. Breiman, L. Bagging Predictors. Mach. Learn., 1996, 24, 123-140.
47. Breiman, L.; Friedman, J.; Stone, C.J.; Olshen, R.A. Classification and Regression Trees; CRC press, 1984.
48. Chang, C.-C.; Lin, C.-J. LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol., 2011, 2, 27.
49. Kotsiantis, S.; Kanellopoulos, D.; Pintelas, P. Handling Imbalanced Datasets: A Review. GESTS Int. Trans. Comput. Sci. Eng., 2006, 30, 25-36.
50. US EPA, O. of W.C. and O. of E.I. EPA ACTOR Downloads http://actor.epa.gov/actor/faces/Download.jsp (accessed Sep 21, 2013).
51. Sushko, I.; Salmina, E.; Potemkin, V.A.; Poda, G.; Tetko, I. V. ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions. J. Chem. Inf. Model., 2012, 52, 2310-2316.
52. Vorberg, S.; Tetko, I. V. Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM). Mol. Inform., 2014, 33, 73-85.
53. Tetko, I. V; Novotarskyi, S.; Sushko, I.; Ivanov, V.; Petrenko, A.E.; Dieden, R.; Lebon, F.; Mathieu, B. Development of Dimethyl Sulfoxide Solubility Models Using 163,000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions. J. Chem. Inf. Model., 2013, 53, 1990-2000.
54. Daylight Theory: SMARTS-A Language for Describing Molecular Patterns http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (accessed Jan 18, 2015).
55. Schorpp, K.; Rothenaigner, I.; Salmina, E.; Reinshagen, J.; Low, T.; Brenke, J.K.; Gopalakrishnan, J.; Tetko, I. V; Gul, S.; Hadian, K. Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens. J. Biomol. Screen., 2014, 19, 715-726.
56. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; John Wiley & Sons, 2009.
57. Tetko, I. V; Tanchuk, V.Y.; Villa, A.E.P. Prediction of NOctanol/water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices. J. Chem. Inf. Comput. Sci., 2001, 41, 1407-1421.
58. Tetko, I. V; Tanchuk, V.Y.; Kasheva, T.N.; Villa, A.E. Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices. J. Chem. Inf. Comput. Sci., 2001, 41, 1488-1493.
59. Hall, L.H.; Kier, L.B.; Brown, B.B. Molecular Similarity Based on Novel Atom-Type Electrotopological State Indices. J. Chem. Inf. Comput. Sci., 1995, 35, 1074-1080.
60. Huuskonen, J.; Livingstone, D.; Tetko, I. Neural Network Modeling for Estimation of Partition Coefficient Based on Atom-Type Electrotopological State Indices. J. Chem. Inf. Comput. Sci., 2000, 40, 947-955.
61. Roy, K.; Roy, P.P. QSAR of Cytochrome Inhibitors. Expert Opin. Drug Metab. Toxicol., 2009, 5, 1245-1266.
62. Lewis, D.F. V; Modi, S.; Dickins, M. Structure-Activity Relationship for Human Cytochrome P450 Substrates and Inhibitors. Drug Metab. Rev., 2002, 34, 69-82.
63. Novotarskyi, S.; Sushko, I.; Korner, R.; Pandey, A.K.; Tetko, I. V. A Comparison of Different QSAR Approaches to Modeling CYP450 1A2 Inhibition. J. Chem. Inf. Model., 2011, 51, 1271-1280.
64. Fourches, D.; Tropsha, A. Using Graph Indices for the Analysis and Comparison of Chemical Datasets. Mol. Inform., 2013, 32, 827-842.
65. Tropsha, A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Inform., 2010, 29, 476-488.
66. Golbraikh, A.; Muratov, E.; Fourches, D.; Tropsha, A. Data Set Modelability by QSAR. J. Chem. Inf. Model., 2014, 54, 1-4.
67. Ertl, P.; Rohde, B.; Selzer, P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J. Med. Chem., 2000, 43, 3714-3717.
68. CDK Small Molecule Descriptors-documentation http://cheminfo.informatics.indiana.edu/~rguha/code/java/nightly/a pi/org/openscience/cdk/qsar/descriptors/molecular/(accessed Sep 8, 2014).
69. Introduction to Calculator Plugins-Calculator Plugins-ChemAxon-DOCS https://docs.chemaxon.com/display/CALCPLUGS/Introduction+to +Calculator+Plugins (accessed Jan 9, 2015).
70. Aires-de-Sousa, J.; Gasteiger, J. New Description of Molecular Chirality and Its Application to the Prediction of the Preferred Enantiomer in Stereoselective Reactions. J. Chem. Inf. Comput. Sci., 2001, 41, 369-375.
71. Varnek, A.; Fourches, D.; Horvath, D.; Klimchuk, O.; Gaudin, C.; Vayer, P.; Solovev, V.; Hoonakker, F.; Tetko, I. V; Marcou, G. ISIDA-Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors. Curr. Comput. Aided. Drug Des., 2008, 4, 191-198.
72. Steinbeck, C.; Han, Y.; Kuhn, S.; Horlacher, O.; Luttmann, E.; Willighagen, E. The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics. J. Chem. Inf. Comput. Sci., 2003, 43, 493-500.
73. Cherkasov, A.; Ban, F.; Santos-Filho, O.; Thorsteinson, N.; Fallahi, M.; Hammond, G.L. An Updated Steroid Benchmark Set and Its Application in the Discovery of Novel Nanomolar Ligands of Sex Hormone-Binding Globulin. J. Med. Chem., 2008, 51, 2047-2056.
74. Potemkin, V.A.; Grishina, M.A. A New Paradigm for Pattern Recognition of Drugs. J. Comput. Aided. Mol. Des., 2008, 22, 489-505.
75. Grishina, M.A.; Bartashevich, E. V; Potemkin, V.A.; Belik, A. V. Genetic Algorithm for Predicting Structures and Properties of Molecular Aggregates in Organic Substances. J. Struct. Chem., 2002, 43, 1040-1044.
76. Potemkin, V.A.; Pogrebnoy, A.A.; Grishina, M.A. Technique for Energy Decomposition in the Study of "Receptor-Ligand" Complexes. J. Chem. Inf. Model., 2009, 49, 1389-1406.
77. Thormann, M.; Vidal, D.; Almstetter, M.; Pons, M. Nomen Est Omen: Quantitative Prediction of Molecular Properties Directly from IUPAC Names. Open Appl. Informatics J., 2007, 1, 28-32.
78. Bultinck, P.; Langenaeker, W.; Lahorte, P.; De Proft, F.; Geerlings, P.; Van Alsenoy, C.; Tollenaere, J.P. The Electronegativity Equalization Method II: Applicability of Different Atomic Charge Schemes. J. Phys. Chem. A, 2002, 106, 7895-7901.
79. Bultinck, P.; Langenaeker, W.; Carbó-Dorca, R.; Tollenaere, J.P. Fast Calculation of Quantum Chemical Molecular Descriptors from the Electronegativity Equalization Method. J. Chem. Inf. Comput. Sci., 2003, 43, 422-428.
80. ADRIANA.Code-Calculation of Molecular Descriptors | Inspiring Chemical Discovery http://www.molecularnetworks.com/products/adrianacode (accessed Sep 28, 2013).