Toxicological and Ecotoxicological Studies for Additives
As the world has become ever more industrialized, an alarmingly large number of chemicals have entered as contaminant mixtures in waste, air, water, and soil.
The need to decrease costs and reduce animal suffering for chemical risk assessment has ever more encouraged the use of methods alternative to the use of animals to predict toxicity. These alternative methods can be generally divided into two subgroups: study of toxicity in laboratory tubes on small organisms (in vitro) and computational techniques (in silico).
These techniques have recently become more important due to mandates such as the categorization of the Canadian Domestic Substance List [Canadian Domestic Substance List; Canadian Environmental Protection Act, 1999 (Section 74, CEPA 1999)], the European Union’s REACH [REACH; REGULATION (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives [91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC], and Cosmetics Regulations [European Union’s Cosmetics Regulations; REGULATION (EC) No 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products], the Japanese Chemical Substance Control [Japanese Chemical Substance Control Act on the Evaluation of Chemical Substances and Regulation of Their Manufacture, etc. (Act No. 117 of October 16, 1973 amended in 2009)], as well as their continued use by the U.S. Environmental Protection Agency (EPA) [Kavlock R, Dix D. J Toxicol Environ Health B Crit Rev 13(2–4):197–217, 2010] and U.S. Food and Drug Administration (FDA) [U.S. Environmental Protection Agency (EPA) and U.S. Food and Drug Administration (FDA); TSCA (1976) Toxic Substances Control Act. United States Publ. Law 94–469, 90 Stat 2003, USA].
Day by day, the number of scientific works and techniques based on in vitro tools has increased their relevancy, supporting the hypothesis of the use of in vitro models as refinement technique due to their ability to provide information on central events involved in toxicant mode of action.
In vitro tools could be used alone or in test batteries with increased potency of the description of cellular events and changes. The chapter provides a brief introduction on the components of an in vitro system, the main differences between models for research and models for testing and a list of validated alternative methods according to the European Centre for the Validation of Alternative Methods (ECVAM) (http://ecvam.jrc.it/, http://ecvam.jrc.ec.europa.eu/) evaluation.
Furthermore, in recent years more and more studies have been carried out in which computational programs have been used to predict the toxicity of chemical compounds. The main driving force behind this trend has been the emergence of new chemical descriptors, algorithms, and statistical perspectives, in addition to the higher expectations as to how such programs can have specific applications, such as for regulatory purposes or drug discovery (Benfenati E. Chem Cent J 1:32, 2007). The performance of the computational models discussed in this chapter relates to the chemical information available and nature of mathematical algorithms. Obtaining a good quality QSAR model depends on many factors, such as the quality of biological data and the choice of descriptors and statistical methods. As a consequence, the uncertainty of the QSAR-predictions is a combination of experimental uncertainties and model uncertainties (Computational chemistry: risk assessment for pharmaceutical and environmental chemicals, edited by Ekins S, WILEY Series on Technologies for the Pharmaceutical Industry, 2007). In this chapter we will consider the applications of QSAR models, and see how interactions are possible between different computational techniques, as well as in vivo and in vitro methods.
KeywordsAlternatives to animal testing Computational toxicology In silico In vitro Predictive models QSAR models Regulation
- 1.RISKCYCLE project, Deliverable 4.1. – Report containing a discussion on the identified criteria and their scores for alternative methods; http://www.wadef.com/projects/riskcycle/Deliverable__Report_4-1.Work_Results.pdf
- 2.REACH; REGULATION (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/ECGoogle Scholar
- 3.European Union’s Cosmetics Regulations; REGULATION (EC) No 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic productsGoogle Scholar
- 4.Russell WMS, Burch RL (1959) The principles of humane experimental technique. Methuen & Co. Ltd., London, (Revised: 1992, Universities Federation for Animal Welfare, Herts, England.) http://altweb.jhsph.edu/publications/humane_exp/het-toc.htm Google Scholar
- 6.U.S. Environmental Protection Agency (EPA) and U.S. Food and Drug Administration (FDA); TSCA (1976) Toxic Substances Control Act. United States Publ. Law 94–469, 90 Stat 2003, USAGoogle Scholar
- 8.Frazier JM (1990) Multiple endpoint measurements to evaluate the intrinsic cellular toxicity of chemicals. J Mol Cell Toxicol 3:349–357Google Scholar
- 9.Ekins S (2007) Computational chemistry: risk assessment for pharmaceutical and environmental chemicals. Wiley, Hoboken, New JerseyGoogle Scholar
- 10.OECD (2007) Guidance document on the validation of (Q)SAR models. http://www.oecd.org/document/2/0,3746,en_2649_34379_42926338_1_1_1_1,00.html
- 11.Helma C (2005) In silico predictive toxicology: the state-of-the-art and strategies to predict human health effects. Curr Opin Drug Discov Devel 8(1):27–31Google Scholar
- 15.Worth AP, Balls M (eds) (2002) Alternative (non-animal) methods for chemicals testing: current status and future prospects. A report prepared by ECVAM and the ECVAM working group on chemicals. ATLA 30(Suppl 1), pp 125Google Scholar
- 16.Milan C, Schifanella O, Roncaglioni A, Benfenati E (2011) Comparison and possible use of in silico tools for carcinogenicity within REACH legislation. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 29(4):300–323Google Scholar