Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): Part II: Corrosion potential

Regulatory Toxicology and Pharmacology

Volumn 71, Issue 2, 2015, Pages 331-336

  Verma, Rajeshwar P ^a   Matthews, Edwin J ^a  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

Artificial neural network; C QSAR; Eye corrosion; QSAR 21; Weight of evidence

Indexed keywords

COSMETIC; DOMESTIC CHEMICAL; DRUG; INDUSTRIAL CHEMICAL; PESTICIDE; CAUSTIC AGENT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL ANALYSIS; CHEMICAL STRUCTURE; COMPUTER MODEL; COMPUTER PREDICTION; COMPUTER PROGRAM; EXPERIMENTAL ANIMAL; EYE CORROSION; EYE INJURY; EYE IRRITATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SENSITIVITY ANALYSIS; TOXICITY TESTING; ANIMAL; ANIMAL TESTING ALTERNATIVE; CHEMICALLY INDUCED; PROCEDURES; RABBIT;

ANIMALIA; ORYCTOLAGUS CUNICULUS;

ANIMAL TESTING ALTERNATIVES; ANIMALS; CAUSTICS; COSMETICS; EYE INJURIES; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RABBITS;

EID: 84923006482     PISSN: 02732300     EISSN: 10960295     Source Type: Journal    
DOI: 10.1016/j.yrtph.2014.12.004     Document Type: Article

References (23)

1. Adler S., Basketter D., Creton S., Pelkonen O., van Benthem J., Zuang V., Andersen K.E., Angers-Loustau A., Aptula A., Bal-Price A., Benfenati E., Bernauer U., Bessems J., Bois F.Y., Boobis A., Brandon E., Bremer S., Broschard T., Casati S., Coecke S., Corvi R., Cronin M., Daston G., Dekant W., Felter S., Grignard E., Gundert-Remy U., Heinonen T., Kimber I., Kleinjans J., Komulainen H., Kreiling R., Kreysa J., Leite S.B., Loizou G., Maxwell G., Mazzatorta P., Munn S., Pfuhler S., Phrakonkham P., Piersma A., Poth A., Prieto P., Repetto G., Rogiers V., Schoeters G., Schwarz M., Serafimova R., Tähti H., Testai E., van Delft J., van Loveren H., Vinken M., Worth A., Zaldivar J.M. Alternative (non-animal) methods for cosmetics testing: current status and future prospects. Arch. Toxicol. 2010, 85:367-485.
2. ADMET Predictor™ User Manual, March 21, 2013 - Version 6.5, 180-181.
3. Andersen F.A. Final report on the safety assessment of ascorbyl palmitate, ascorbyl dipalmitate, ascorbyl stearate, erythorbic acid, and sodium erythorbate. Inter. J. Toxicol. 1999, 18(Suppl. 3):1-26.
4. Barratt M.D. QSARs for the eye irritation potential of neutral organic chemicals. Toxicol. In Vitro 1997, 11:1-8.
5. Cho S.A., An S., Lee E., Shin K., Cho J.C., Lee T.R. A new cell-based method for assessing the eye irritation potential of chemicals: an alternative to the Draize test. Toxicology Lett. 2012, 212:198-204.
6. CIR (Cosmetic Ingredient Review) Final report on the safety assessment of cetearyl alcohol, cetyl alcohol, isostearyl alcohol, myristyl alcohol, and behenyl alcohol. Inter. J. Toxicol. 1988, 7:359-413.
7. CIR (Cosmetic Ingredient Review) Final report on the safety assessment of tocopherol, tocopheryl acetate, tocopheryl linoleate, tocopheryl linoleate/oleate, tocopheryl nicotinate, tocopheryl succinate, dioleyl tocopheryl, methylsilanol, potassium ascorbyl tocopheryl phosphate, and tocophersolan. Inter. J. Toxicol. 2002, 21(Suppl. 3):51-116.
8. Scientific Literature Review: Citric Acid and Its Inorganic Salts and Alkyl and Glycol Esters as Used in Cosmetics 2011, CIR (Cosmetic Ingredient Review), CIR publication, pp. 1--43.
9. CIR (Cosmetic Ingredient Review), 2012. Alkyl Esters Re-review - Supplement Book 1: Cetyl esters and other previously reviewed alkyl esters. CIR Expert Panel Meeting, March 5-6, 2012. pp.1--310. http://www.cir-safety.org/sites/default/files/Supplement%20Book%201.pdf.
10. Curren R.D., Harbell J.W. Ocular safety: a silent (in vitro) success story. Altern. Lab. Anim. 2002, 30(Suppl. 2):69-74.
11. EC (European Community) Directive 2003/15/EC of the European Parliament and of the Council of 27 February 2003 amending Council Directive 76/768/EEC on the approximation of the laws of the member states relating to cosmetic products. Official J. Eur. Union 2003, L66:26-35.
12. EEC (European Economic Community) Annex V-methods for the determination of toxicity. B.5. Acute toxicity (eye irritation). Official J. Eur. Communities 1992, L383:A 35.
13. Jaworska J., Nikolova-Jeliazkova N., Aldenberg T. QSAR applicability domain estimation by projection of the training set in descriptor space: a review. ATLA 2005, 33:445-459.
14. Nikolova-Jeliazkova N., Jaworska J. An approach to determining applicability domains for QSAR contribution models: an analysis of SRC KOWWIN. ATLA 2005, 33:461-470.
15. OECD (Organization for Economic Co-operation and Development), 2002. OECD Guidelines for the Testing of Chemicals No. 405: Acute Eye Irritation/Corrosion. 14 pp. Paris, France.
16. Saliner, A.G., Patlewicz, G., Worth, A.P., 2006. Review of Literature-Based Models for Skin and Eye Irritation and Corrosion. JRC Technical EUR Report. EUR 22320 EN, 104pp. Ispra, Italy: European Commission, Joint Research Centre.
17. Saliner, A.G., Worth, A.P., 2007. Testing Strategies for the Prediction of Skin and Eye Irritation and Corrosion for Regulatory Purposes. JRC Technical EUR Report. EUR 22881 EN, 46pp. Luxembourg, Italy: European Commission, Joint Research Centre.
18. Sugai S., Murata K., Kitagaki T., Tomita I. Studies on eye irritation caused by chemicals in rabbits - 1. A quantitative structure-activity relationships approach to primary eye irritation of chemicals in rabbits. J. Toxicol. Sci. 1990, 15:245-262.
19. Tetko I.V. Neural network studies. 4. Introduction to associative neural networks. J. Chem. Inf. Comput. Sci. 2002, 42:717-728.
20. UNECE (United Nations Economic Commission for Europe), 2004. Globally Harmonized System of Classification and Labeling of Chemicals (GHS). Part 3, Health and environmental hazards. Chapter 3.3. Serious eye damage/eye irritation. < http://www.unece.org/fileadmin/DAM/trans/danger/publi/ghs/ghs_rev00/English/GHS-PART-3e.pdf.
21. Verma R.P., Matthews E.J. Estimation of the chemical-induced eye injury using Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): Part I: Irritation potential. Regul. Toxicol. Pharmacol. 2014, 71:318-330.
22. Weil C.S., Scala R.A. Study of intra- and interlaboratory variability in the results of rabbit eye and skin irritation tests. Toxicol. Appl. Pharmacol. 1971, 19:276-360.
23. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 1988, 28:31-36.