Consensus of classification trees for skin sensitisation hazard prediction

Toxicology in Vitro

Volumn 36, Issue , 2016, Pages 197-209

  Asturiol, D ^a   Casati, S ^a   Worth, A ^a  

^a JOINT RESEARCH CENTRE   (Italy)

Author keywords

Decision tree; In silico; In vitro; Prediction; QSAR; Skin sensitisation

Indexed keywords

ANIMAL MODEL; CLASSIFICATION; COMPUTER PROGRAM; CONSENSUS DEVELOPMENT; DECISION TREE; DISEASE MODEL; EXERCISE; IN VITRO STUDY; LOCAL LYMPH NODE ASSAY; PREDICTION; SKIN SENSITIZATION; BIOLOGICAL MODEL; COMPUTER SIMULATION; CONSENSUS; SENSITIVITY AND SPECIFICITY; SKIN; SKIN ALLERGY;

HAPTEN;

COMPUTER SIMULATION; CONSENSUS; DERMATITIS, ALLERGIC CONTACT; HAPTENS; LOCAL LYMPH NODE ASSAY; MODELS, BIOLOGICAL; SENSITIVITY AND SPECIFICITY; SKIN;

EID: 84982095736     PISSN: 08872333     EISSN: 18793177     Source Type: Journal    
DOI: 10.1016/j.tiv.2016.07.014     Document Type: Article

References (77)

1. Alves, V.M., Muratov, E., Fourches, D., Strickland, J., Andrade, C.H., Tropsha, A., Hill, C., Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol. Appl. Pharmacol. 284 (2015), 262–272, 10.1016/j.taap.2014.12.014.
2. Aptula, A.O., Patlewicz, G., Roberts, D.W., Skin sensitization: reaction mechanistic applicability domains for structure–activity relationships. Chem. Res. Toxicol. 18 (2005), 1420–1426, 10.1021/tx050075m.
3. Ashikaga, T., Yoshida, Y., Hirota, M., Yoneyama, K., Itagaki, H., Sakaguchi, H., Miyazawa, M., Ito, Y., Suzuki, H., Toyoda, H., Development of an in vitro skin sensitization test using human cell lines: the human Cell Line Activation Test (h-CLAT): I. Optimization of the h-CLAT protocol. Toxicol. in Vitro 20 (2006), 767–773, 10.1016/j.tiv.2005.10.012.
4. Basketter, D.A., Alépée, N., Ashikaga, T., Barroso, J., Gilmour, N., Goebel, C., Hibatallah, J., Hoffmann, S., Kern, P., Martinozzi-Teissier, S., Maxwell, G., Reisinger, K., Sakaguchi, H., Schepky, A., Tailhardat, M., Templier, M., Categorization of chemicals according to their relative human skin sensitizing potency. Dermatitis 25 (2014), 11–21, 10.1097/DER.0000000000000003.
5. Bauch, C., Kolle, S.N., Ramirez, T., Eltze, T., Fabian, E., Mehling, A., Teubner, W., van Ravenzwaay, B., Landsiedel, R., Putting the parts together: combining in vitro methods to test for skin sensitizing potentials. Regul. Toxicol. Pharmacol. 63 (2012), 489–504, 10.1016/j.yrtph.2012.05.013.
6. Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B., {KNIME}: The {k}onstanz {i}nformation {m}iner. Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007), 2007, Springer.
7. Consonni, V., Todeschini, R., Pavan, M., Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J. Chem. Inf. Comput. Sci. 42 (2002), 682–692, 10.1021/ci015504a.
8. Consonni, V., Todeschini, R., Pavan, M., Gramatica, P., Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR S tudies. J. Chem. Inf. Comput. Sci. 42 (2002), 693–705, 10.1021/ci0155053.
9. Cooper, J.A., Saracci, R., Cole, P., Describing the validity of carcinogen screening tests. Br. J. Cancer 39 (1979), 87–89, 10.1038/bjc.1979.10.
10. Devinyak, O., Havrylyuk, D., Lesyk, R., 3D-MoRSE descriptors explained. J. Mol. Graph. Model. 54 (2014), 194–203, 10.1016/j.jmgm.2014.10.006.
11. Dimitrov, S., Dimitrova, G., Pavlov, T., Dimitrova, N., Patlewicz, G., Niemela, J., Mekenyan, O., A stepwise approach for defining the applicability domain of SAR and QSAR models. J. Chem. Inf. Model. 45 (2005), 839–849, 10.1021/ci0500381.
12. Dimitrov, S., Low, L., Patlewicz, G., Kern, P., Dimitrova, G., Comber, M., Phillips, R., Niemela, J., Bailey, P., Mekenyan, O., Skin sensitization: modeling based on skin metabolism simulation and formation of protein conjugates. Int. J. Toxicol. 24 (2005), 189–204.
13. Dumont, C., Barroso, J., Matys, I., Worth, A., Casati, S., Analysis of the local lymph node assay (LLNA) variability for assessing the prediction of skin sensitisation potential and potency of chemicals with non-animal approaches. Toxicol. in Vitro 34 (2016), 220–228, 10.1016/j.tiv.2016.04.008.
14. EC, REACH Regulation (1907/2006/EC): Authorisation and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, Amending Directive 1999. 2006.
15. EC, EU Cosmetic Products Regulation (1223/2009/EC). 2009.
16. Emter, R., Ellis, G., Natsch, A., Performance of a novel keratinocyte-based reporter cell line to screen skin sensitizers in vitro. Toxicol. Appl. Pharmacol. 245 (2010), 281–290, 10.1016/j.taap.2010.03.009.
17. Enoch, S.J., Ellison, C.M., Schultz, T.W., Cronin, M.T.D., A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity. Crit. Rev. Toxicol. 41 (2011), 783–802, 10.3109/10408444.2011.598141.
18. EURL ECVAM, Recommendation on the Direct Peptide Reactivity Assay (DPRA) for Skin Sensitisation Testing. 2013, 10.2788/48229.
19. EURL ECVAM, Recommendation on the KeratinoSensTM Assay for Skin Sensitisation Testing. 2014, 10.1017/CBO9781107415324.004.
20. EURL ECVAM, Recommendation on the Human Cell Line Activation Test (h-CLAT) for Skin Sensitisation Testing. 2015, 10.2788/29986.
21. EURL-ECVAM, Direct Peptide Reactivity Assay (DPRA) ECVAM Validation Study Report. 2012.
22. EURL-ECVAM, KeratinoSens™ Validation Study Reports. 2014.
23. EURL-ECVAM, h-CLAT Validation Study Reports. 2015.
24. Gerberick, G.F., Vassallo, J.D., Bailey, R.E., Chaney, J.G., Morrall, S.W., Lepoittevin, J.P., Development of a peptide reactivity assay for screening contact allergens. Toxicol. Sci. 81 (2004), 332–343, 10.1093/toxsci/kfh213.
25. Gerberick, G.F., Vassallo, J.D., Foertsch, L.M., Price, B.B., Chaney, J.G., Lepoittevin, J.P., Quantification of chemical peptide reactivity for screening contact allergens: a classification tree model approach. Toxicol. Sci. 97 (2007), 417–427, 10.1093/toxsci/kfm064.
26. Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning. first ed., 1989, Boston, MA, USA.
27. Guyard-Nicodème, M., Gerault, E., Platteel, M., Peschard, O., Veron, W., Mondon, P., Pascal, S., Feuilloley, M.G.J., Development of a multiparametric in vitro model of skin sensitization. J. Appl. Toxicol., 48–58, 2015, 10.1002/jat.2986.
28. Hall, M.A., Correlation-based Feature Subset Selection for Machine Learning. 1998, Hamilton, New Zealand.
29. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software. SIGKDD Explor, 11, 2009.
30. Hirota, M., Kouzuki, H., Ashikaga, T., Sono, S., Tsujita, K., Sasa, H., Aiba, S., Artificial neural network analysis of data from multiple in vitro assays for prediction of skin sensitization potency of chemicals. Toxicol. in Vitro 27 (2013), 1233–1246, 10.1016/j.tiv.2013.02.013.
31. Hirota, M., Fukui, S., Okamoto, K., Kurotani, S., Imai, N., Fujishiro, M., Kyotani, D., Kato, Y., Kasahara, T., Fujita, M., Toyoda, A., Sekiya, D., Watanabe, S., Seto, H., Takenouchi, O., Ashikaga, T., Miyazawa, M., Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization. J. Appl. Toxicol., 2015, 1333–1347, 10.1002/jat.3105.
32. Ideaconsult Ltd. on behalf of the JRC, Toxtree. 2005.
33. Istituto di Ricerche Farmacologiche Mario Negri, Vega. 2013.
34. Janežič, D., Miličević, A., Nikolić, S., Trinajstić, N., Graph Theoretical Matrices in Chemistry, Mathemat. third ed., 2007, CRC Press.
35. Jaworska, J., Integrating non-animal test information into an adaptive testing strategy – skin sensitization proof of concept case. ALTEX 28 (2011), 211–225, 10.14573/altex.2011.3.211.
36. Jaworska, J., Dancik, Y., Kern, P., Gerberick, F., Natsch, A., Bayesian integrated testing strategy to assess skin sensitization potency: from theory to practice. J. Appl. Toxicol. 33 (2013), 1353–1364, 10.1002/jat.2869.
37. Jaworska, J., Andreas, N., Ryan, C., Strickland, J., Takao, A., Masaaki, M., Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy. Arch. Toxicol. 89 (2015), 2355–2383, 10.1007/s00204-015-1634-2.
38. Jowsey, I.R., Basketter, D.A., Westmoreland, C., Kimber, I., A future approach to measuring relative skin sensitising potency: a proposal. J. Appl. Toxicol. 26 (2006), 341–350, 10.1002/jat.1146.
39. Karlberg, A.T., Bergstrom, M.A., Borje, A., Luthman, K., Nilsson, J.L.G., Allergic contact dermatitis-formation, structural requirements, and reactivity of skin sensitizers. Chem. Res. Toxicol. 21 (2008), 53–69, 10.1021/tx7002239.
40. Landrum, G., RDKit: Open-source Informatics. 2015.
41. LHASA, Nexus. 2015.
42. MacKay, C., Davies, M., Summerfield, V., Maxwell, G., From pathways to people: applying the adverse outcome pathway (AOP) for skin sensitization to risk assessment. ALTEX 30 (2013), 473–486, 10.14573/altex.2013.4.473.
43. Macmillan, D.S., Canipa, S.J., Chilton, M.L., Williams, R.V., Barber, C.G., Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays. Regul. Toxicol. Pharmacol. 76 (2016), 30–38, 10.1016/j.yrtph.2016.01.009.
44. Martin, S.F., New concepts in cutaneous allergy. Contact Dermatitis 72 (2015), 2–10, 10.1111/cod.12311.
45. Martin, S.F., Esser, P.R., Weber, F.C., Jakob, T., Freudenberg, M.a., Schmidt, M., Goebeler, M., Mechanisms of chemical-induced innate immunity in allergic contact dermatitis. Allergy 66 (2011), 1152–1163, 10.1111/j.1398-9995.2011.02652.x.
46. Muratov, E.N., Artemenko, A.G., Varlamova, E.V., Polischuk, P.G., Lozitsky, V.P., Fedchuk, A.S., Lozitska, R.L., Gridina, T.L., Koroleva, L.S., Sil'nikov, V.N., Galabov, A.S., Makarov, V.A., Riabova, O.B., Wutzler, P., Schmidtke, M., Kuz'min, V.E., Per aspera ad astra: application of Simplex QSAR approach in antiviral research. Future Med. Chem. 2 (2010), 1205–1226, 10.4155/fmc.10.194.
47. Natsch, A., Emter, R., Skin sensitizers induce antioxidant response element dependent genes: application to the in vitro testing of the sensitization potential of chemicals. Toxicol. Sci. 102 (2008), 110–119, 10.1093/toxsci/kfm259.
48. Natsch, A., Emter, R., Ellis, G., Filling the concept with data: integrating data from different in vitro and in silico assays on skin sensitizers to explore the battery approach for animal-free skin sensitization testing. Toxicol. Sci. 107 (2009), 106–121, 10.1093/toxsci/kfn204.
49. Natsch, A., Ryan, C.A., Foertsch, L., Emter, R., Jaworska, J., Gerberick, F., Kern, P., A dataset on 145 chemicals tested in alternative assays for skin sensitization undergoing prevalidation. J. Appl. Toxicol. 33 (2013), 1337–1352, 10.1002/jat.2868.
50. Natsch, A., Emter, R., Gfeller, H., Haupt, T., Ellis, G., Predicting skin sensitizer potency based on in vitro data from KeratinoSens and kinetic peptide binding: global versus domain-based assessment. Toxicol. Sci. 143 (2015), 319–332, 10.1093/toxsci/kfu229.
51. Nukada, Y., Miyazawa, M., Kazutoshi, S., Sakaguchi, H., Nishiyama, N., Data integration of non-animal tests for the development of a test battery to predict the skin sensitizing potential and potency of chemicals. Toxicol. in Vitro 27 (2013), 609–618, 10.1016/j.tiv.2012.11.006.
52. O'Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., Hutchison, G.R., Open Babel: an open chemical toolbox. J. Chem. Inf., 3, 2011, 33, 10.1186/1758-2946-3-33.
53. OECD, OECD Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationships Models., OECD. 2004.
54. OECD, The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins. Part 1: Scientific Evidence. Ser. Test. Assessment, No. 168, ENV/JM/MONO(2012) 10 PART 1. 2012.
55. OECD, The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins. Part 2: Use of the AOP to Develop Chemical Categories and Integrated Assessment and Testing Approaches. Ser. Test. Assessment, No. 168, ENV/JM/MONO(2012) 10 PART 2. 2012.
56. OECD, OECD QSAR Application Toolbox. 2013.
57. OECD, OECD Guideline for Testing of Chemicals - Guideline 422C: In Chemico Skin Sensitisation Direct Peptide Reactivity Assay (DPRA). 2015.
58. OECD, OECD Guideline for Testing of Chemicals - Guideline 442D: In Vitro Skin Sensitisation : ARE-Nrf2 Luciferase Test. 2015.
59. OECD, OECD Guidance Document on the Reporting of Defined Approaches to be Used Within Integrated Approaches to Testing and Assessment (No. ENV/JM/MONO(2016)28). 2016.
60. OECD, OECD Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to be Used Within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitization (No. ENV/JM/MONO(2016)29). 2016.
61. Patlewicz, G., Kuseva, C., Mehmed, A., Popova, Y., Dimitrova, G., Ellis, G., Hunziker, R., Kern, P., Low, L., Ringeissen, S., Roberts, D., Mekenyan, O., TIMES-SS – recent refinements resulting from an industrial skin sensitisation consortium. SAR QSAR Environ. Res. 25 (2014), 367–391, 10.1080/1062936X.2014.900520.
62. Sakaguchi, H., Miyazawa, M., Yoshida, Y., Ito, Y., Suzuki, H., Prediction of preservative sensitization potential using surface marker CD86 and/or CD54 expression on human cell line, THP-1. Arch. Dermatol. Res. 298 (2006), 427–437, 10.1007/s00403-006-0714-9.
63. Salzberg, S.L., C4.5: programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993. Mach. Learn. 16 (1994), 235–240, 10.1007/BF00993309.
64. Simulations Plus, Inc., Lancaster, C., ADMET Predictor. 2014.
65. Strickland, J., Zang, Q., Kleinstreuer, N., Paris, M., Lehmann, D.M., Choksi, N., Matheson, J., Jacobs, A., Lowit, A., Allen, D., Casey, W., Integrated decision strategies for skin sensitization hazard. J. Appl. Toxicol. n/a–n/a, 2016, 10.1002/jat.3281.
66. Takenouchi, O., Miyazawa, M., Saito, K., Ashikaga, T., Sakaguchi, H., Predictive performance of the human Cell Line Activation Test (h-CLAT) for lipophilic chemicals with high octanol-water partition coefficients. J. Toxicol. Sci. 38 (2013), 599–609.
67. Takenouchi, O., Fukui, S., Okamoto, K., Kurotani, S., Imai, N., Fujishiro, M., Kyotani, D., Kato, Y., Kasahara, T., Fujita, M., Toyoda, A., Sekiya, D., Watanabe, S., Seto, H., Hirota, M., Ashikaga, T., Miyazawa, M., Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK based on a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals. J. Appl. Toxicol., 2015, 1318–1332, 10.1002/jat.3127.
68. Talete Srl, DRAGON (Software for Molecular Descriptor Calculation). 2010.
69. Teubner, W., Mehling, A., Schuster, P.X., Guth, K., Worth, A., Burton, J., van Ravenzwaay, B., Landsiedel, R., Computer models versus reality: how well do in silico models currently predict the sensitization potential of a substance. Regul. Toxicol. Pharmacol. 67 (2013), 468–485, 10.1016/j.yrtph.2013.09.007.
70. Todeschini, R., Gramatica, P., 3D-modelling and prediction by WHIM descriptors. Part 5. Theory development and chemical meaning of WHIM descriptors. Quant. Struct. Relationships, 16, 1997, 113–119.
71. Todeschini, R., Lasagni, M., Marengo, E., New molecular descriptors for 2D and 3D structures. Theory. J. Chemom. 8 (1994), 263–272, 10.1002/cem.1180080405.
72. Tong, W., Hong, H., Fang, H., Xie, Q., Perkins, R., Decision forest: combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 43 (2003), 525–531, 10.1021/ci020058s.
73. Tong, W., Hong, H., Xie, Q., Shi, L., Fang, H., Perkins, R., Assessing QSAR limitations - a regulatory perspective. Curr. Comput. Aided Drug Des. 1 (2005), 195–205, 10.2174/1573409053585663.
74. Tsujita-Inoue, K., Hirota, M., Ashikaga, T., Atobe, T., Kouzuki, H., Aiba, S., Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assays. Toxicol. in Vitro 28 (2014), 626–639, 10.1016/j.tiv.2014.01.003.
75. Urbisch, D., Mehling, A., Guth, K., Ramirez, T., Honarvar, N., Kolle, S., Landsiedel, R., Jaworska, J., Kern, P.S., Gerberick, F., Natsch, A., Emter, R., Ashikaga, T., Miyazawa, M., Sakaguchi, H., Assessing skin sensitization hazard in mice and men using non-animal test methods. Regul. Toxicol. Pharmacol. 71 (2015), 337–351, 10.1016/j.yrtph.2014.12.008.
76. van der Veen, J.W., Rorije, E., Emter, R., Natsch, A., van Loveren, H., Ezendam, J., Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals. Regul. Toxicol. Pharmacol. 69 (2014), 371–379, 10.1016/j.yrtph.2014.04.018.
77. Witten, I.H., Frank, E., Data Mining: Practical Machine Learning Tools and Techniques. second ed., 2005.