Development of classification-and regression-based QSAR models and in silico screening of skin sensitisation potential of diverse organic chemicals

Molecular Simulation

Volumn 40, Issue 4, 2014, Pages 261-274

  Nandy, Ashis ^a   Kar, Supratik ^a   Roy, Kunal ^a  

^a JADAVPUR UNIVERSITY   (India)

Author keywords

EC3; LDA; OECD; QSAR; skin sensitisation

Indexed keywords

EC3; LDA; OECD; QSAR; SENSITISATION;

ENVIRONMENTAL TECHNOLOGY; HEALTH HAZARDS; INDUSTRIAL CHEMICALS; INDUSTRIAL HYGIENE; MATHEMATICAL MODELS; MOLECULAR GRAPHICS; MOLECULES; ORGANIC CHEMICALS; T-CELLS;

COMPUTATIONAL CHEMISTRY;

EID: 84891842961     PISSN: 08927022     EISSN: 10290435     Source Type: Journal    
DOI: 10.1080/08927022.2013.801076     Document Type: Article

References (67)

1. Karlberg T, Bergstrom MA, Borje A, Luthman K, Nilsson JL. Allergic contact dermatitis-formation, structural requirements, and reactivity of skin sensitizers. Chem Res Toxicol. 2008;21:53-69.
2. del Savio B, Sherertz EF. Is allergic contact dermatitis being overlooked? Arch Fam Med. 1994;3:537-543.
3. Li Y, Tseng YJ, Pan D, Liu J, Kern PS, Gerberick GF, Hopfinger AJ. 4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures. Chem Res Toxicol. 2007;20:114-128.
4. Divkovic M, Pease CK, Gerberick GF, Basketter DA. Haptenprotein binding: from theory to practical application in the in vitro prediction of skin sensitization. Contact Dermatitis. 2005;53:189-200.
5. Bonefeld CM, Larsen JM, Dabelsteen S, Geisler C, White IR, Menne T, Johansen JD. Consumer available permanent hair dye products cause major allergic immune activation in an animal model. J Dermatol. 2010;162:102-107.
6. Jenkinson C, Jenkins RE, Maggs JL, Kitteringham NR, Aleksic M, Park BK, Naisbitt DJ. A mechanistic investigation into the irreversible protein binding and antigenicity of p-phenylenediamine. Chem Res Toxicol. 2009;22:1172-1180.
7. Report of the world summit on sustainable development: Johannesburg, South Africa. 26 August-4 September 2002 (with corrigendum), United Nations, 2003 [Internet]. Available from: http://www.johannesburgsummit.org/html/ documents/documents. html
8. Parliament adopts REACH: new EU Chemicals Legislation and New Chemicals Agency. 2006 [Internet]. Available from: http://www.europarl.europa.eu/sides/ getDoc.do?language¼EN&type¼IMPRESS&reference& frac14;20061213IPR01493
9. Benigni R, Giuliani A. Putting the predictive toxicology challenge into perspective: reflections on the results. Bioinformatics. 2003;19:1194-1200.
10. Golla S, Madihally S, Jr Robinson RL, Gasem KAM. Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction. Toxicol In Vitro. 2009;23:454-465.
11. Golla S, Madihally S, Robinson RL, Gasem KAM. Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction. Toxicol In Vitro. 2009;23:454-465.
12. Ashby J, Basketter DA, Paton D, Kimber I. Structure-activity relationships in skin sensitization using the murine local lymph node assay. Toxicology. 1995;103:177-194.
13. Martin SF, Esser PR, Schmucker S, Dietz L, Naisbitt DJ, Park BK, Vocanson M, Nicolas JF, Keller M, Pichler WJ. T-cell recognition of chemicals, protein allergens and drugs: towards the development of in vitro assays. Cell Mol Life Sci. 2010;67:4171-4184.
14. Fedorowicz A, Singh H, Soderholm S, Demchuk E. Structure-activity models for contact sensitization. Chem Res Toxicol. 2005;18:954-969.
15. Gerberick F, Aleksic M, Basketter D, Casati S, Karlberg AT, Kern P, Kimber I, Lepoittevin JP, Natsch A, Ovigne JM. Chemical reactivity measurement and the predictive identification of skin sensitisers. The report and recommendations of ECVAM workshop 64. Altern Lab Anim. 2008;36:215-242.
16. European Commission, Directive 2006/121/EC of the European Parliament and of the Council of 18 December 2006 amending Council Directive 67/548/EEC on the approximation of laws, regulations and administrative provisions relating to the classification, packaging and labelling of dangerous substances in order to adapt it to Regulation (EC) No. 1907/2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) and establishing a European Chemicals Agency. Off J Eur Union 2006, L 396/850 of 30.12.2006, Office for Official Publications of the European Communities (OPOCE), Luxembourg.
17. Kar S, Roy K. Predictive toxicology using QSAR: a perspective. J Indian Chem Soc. 2010;87:1455-1515.
18. Chen K, Chang S, Huang H, Lin T, Wu Y, Chen CY. Three-in-one agonists for PPAR-a, PPAR-g, and PPAR-d screened from Traditional Chinese Medicine Database@Taiwan. J Biomol Struct Dyn. 2012;30:662-683.
19. Chang S, Huang H, Chen H, Chen CY. High performance screening, structural and molecular dynamics analysis to identify H1 inhibitors from TCM Database@Taiwan. Mol Biosyst. 2011;7:3366-3374.
20. Organization for Economic Cooperation and Development (OECD). Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships
21. [(Q) SAR] Models. ENV/JM/MONO.2007.2.1-154.
22. Basketter A, Balikie L, Dearman RJ, Kimber I, Ryan CA, Gerberick GF, Harvey P, Evans P, White IR, Rycroft RJ. Use of the local lymph node assay for the estimation of relative contact allergenic potency. Contact Dermatitis. 2000;42:344-348.
23. Basketter A, Evans P, Fielder RJ, Gerberick GF, Dearman RJ, Kimber I. Local lymph node assay-validation, conduct and use in practice. Food Chem Toxicol. 2002;40:593-598.
24. Basketter DA, Gerberick GF, Kimber I, Loveless SE. The local lymph node assay: a viable alternative to currently accepted skin sensitization tests. Food Chem Toxicol. 1996;34:985-997.
25. 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. 2005;45:839-849.
26. Furuhama A, Hasunuma K, Aoki Y, Yoshioka Y, Shiraishi H. Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE). SAR QSAR Environ Res. 2011;22:505-523.
27. Liu J, Kern PS, Gerberick GF, Santos-Filho OA, Esposito EX, Hopfinger AJ, Tseng YJ. Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors. J Comput Aided Mol Des. 2008;22:345-366.
28. Patlewicz G, Roberts DW, Uriarte E. A comparison of reactivity schemes for the prediction skin sensitization potential. Chem Res Toxicol. 2008;21:521-541.
29. Miller MD, Yourtee DM, Glaros AG, Chappelow CC, Eick JD, Holder AJ. Quantum mechanical structure-activity relationship analyses for skin sensitization. J Chem Inf Model. 2005;45:924-929.
30. Gerberick GF. Skin sensitization in chemical risk assessment: report of a WHO/IPCS international workshop focusing on dose-response assessment. Regul Toxicol Pharmacol. 2008;50:155-199.
31. MarvinSketch 5.10.0 [Internet]. 1998-2012. ChemAxon Ltd. Available from: http://www.chemaxon.com
32. Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32:1466-1474.
33. Cerius 2 version 4.10 software Accelrys Inc. [Internet]. San Diego, CA. Available from: http://www.accelrys.com/cerius2
34. DRAGON version 6 TALETE srl. [Internet]. Milan (Italy). Available from: http://www.talete.mi.it/products/dragon-mol ecular-descriptors.htm
35. Roy K. On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opin Drug Discov. 2007;2:1567-1577.
36. Leonard JT, Roy K. On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci. 2006;25:235-251.
37. Roy PP, Leonard JT, Roy K. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemom Intell Lab Syst. 2008;90:31-42.
38. Mitteroecker P, Bookstein F. Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics. Evol Biol. 2011;38:100-114.
39. STATISTICA 7.1 STATSOFT Inc. [Internet]. Tulsa (OK). Available from: http://www.statsoft.com/
40. SPSS Statistical Software SPSS Inc. [Internet]. Chicago (Il). Available from: http://www.spss.com
41. MINITAB is a Statistical Software Minitab Inc. [Internet]. State College (PA). Available from: http://www.minitab.com
42. Roy K, Mitra I. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design. Comb Chem High Throughput Screen. 2011;14:450-474.
43. Gálvez-Llompart M, Recio MC, Garc?́a-Domenech R. Topological virtual screening: a way to find new compounds active in ulcerative colitis by inhibiting NF-kB. Mol Divers. 2011;15:917-926.
44. Prado-Prado FJ, Uriarte E, Borges F, González-D?́az H. Multi-target spectral moments for QSAR and complex networks study of antibacterial drugs. Eur J Med Chem. 2009;44:4516-4521.
45. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27:861-874.
46. Perez-Garrido A, Helguera AM, Borges F, Cordeiro MNDS, Rivero V, Escudero AG. Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models. J Chem Inf Model. 2011;51:2746-2759.
47. Murcia-Soler M, Pérez-Giménez F, Garc?́a-March FJ, Salabert-Salvador MT, D?́az-Villanuev W, Medina-Casamayor P. Discrimination and selection of new potential antibacterial compounds using simple topological descriptors. J Mol Graph Model. 2003;21:375-390.
48. OECD Principles for the Validation of (Q)SARs. [Internet]. [cited 2012 May 9]. Available from: http://www.oecd.org/dataoecd/33/37/37849783
49. OECD, Environment Directorate, Joint Meeting of The Chemicals Committee and The Working Party on Chemicals, Pesticides and Biotechnology. [Internet]. [cited 2012 May 9]. Available from: http://www.olis.oecd.org/olis/2004doc.nsf/ LinkTo/NT00009192/$FILE/JT00176183
50. Golbraikh A, Tropsha A. Beware of q2!. J Mol Graphics Model. 2002;20:269-276.
51. Consonni V, Ballabio D, Todeschini R. Comments on the definition of the Q2 parameter for QSAR validation. J Chem Inf Model. 2009;49:1669-1678.
52. Kar S, Roy K. First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines. Chemosphere. 2012;87:339-355.
53. Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci. 2003;22:69-77.
54. Mitra I, Saha A, Roy K. Exploring quantitative structure-activity relationship (QSAR) studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol Simul. 2010;36:1067-1079.
55. OECD Document. Guidance Document on the Validation of (Quantitative) 1226 Structure-Activity Relationships (Q)SARs] Models. ENV/JM/MONO(2007). 2007;2.
56. Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007;26:694-701.
57. Tsou Y, Chen KC, Lin HC, Chang SS, Chen CYC. Uroporphyrinogen decarboxylase as a potential target for specific components of traditional Chinese medicine: a virtual screening and molecular dynamics study. PLoS ONE. 2012;7:1-10.
58. Chen KY, Chang SS, Chen CYC. In silico identification of potent pancreatic triacylglycerol lipase inhibitors from traditional Chinese medicine. PLoS ONE. 2012;7:1-11.
59. Tou WI, Chen CYC. Traditional Chinese medicine as dual guardians against hypertension and cancer?. J Biomol Struct Dyn. 2012;30:299-317.
60. Tou WI, Chen CYC. In silico investigation of potential Src kinase ligands from traditional Chinese medicine. PLoS ONE. 2012;7:1-15.
61. Chang SS, Huang HJ, Chen CYC. Two birds with one stone? Possible dual-targeting H1N1 inhibitors from traditional Chinese medicine. PLoS ONE. 2011;7:1-16.
62. Yang SC, Chang SS, Chen HY, Chen CYC. Identification of potent EGFR inhibitors from TCM Database@Taiwan. PLoS ONE. 2011;7:1-10.
63. Chen CYC. TCM Database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico. PLoS ONE. 2011;6:1-5.
64. Tsai TY, Chang KW, Chen CYC.Screen: world's first cloudcomputing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J Comput Aided Mol Des. 2011;25:525-531.
65. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs. Nucleic Acids Res. 2011;39:D1035-D1041.
66. Kreiling R, Hollnagel HM, Hareng L, Eigler D, Lee MS, Griem P, Dreeßen B, Kleber M, Albrecht A, Garcia C, Wendel A. Comparison of the skin sensitizing potential of unsaturated compounds as assessed by the murine local lymph node assay (LLNA) and the guinea pig maximization test (GPMT). Food Chem Toxicol. 2008;46:1896-1904.
67. Matsushita T, Kamide R. Five cases of photocontact dermatitis due to topical ketoprofen: photopatch testing and cross-reaction study. Photodermatol Photoimmunol Photomed. 2001;17:26-31.