@book{JRC40140,
	editor = {},
	address = {Heidelberg (Germany)},
	year = {2007},
	author = {Worth A and Netzeva T and Tier G},
	isbn = {978-1-4020-6101-1},
	abstract = {In the last few decades, society has become increasingly concerned about the possible impacts of chemicals to which humans and environmental organisms are exposed. In many industrialised countries, this has led to the implementation of stringent chemicals legislation and to the initiation of ambitious risk assessment and management programmes (see Chapter 1). However, it has become increasingly apparent that the magnitude of the task exceeds the availability of resources (experts, time, money) if traditional test methods are employed. This realization, coupled with increasing attention to animal welfare concerns, has prompted the development and application of various (computer-based) estimation methods in the regulatory assessment of chemicals. Estimation methods include ¿structure-activity relationships¿ (SARs) and ¿quantitative structure-activity relationships¿ (QSARs), which are collectively (and confusingly) referred to as (Q)SARs. These are theoretical models that can be used to predict the physicochemical, biological (e.g. toxicological) and environmental fate properties of molecules from the knowledge of chemical structure. In addition to the (Q)SARs that have been reported in the scientific literature (more than 20,000 models), a number of ¿expert systems¿ have been developed, generally as commercial products. The term ¿expert system¿ refers to a heterogeneous collection of computer-based estimation methods, which are based on the integrated use of databases (containing experimental data) and/or rule bases (containing SARs, QSARs and other decision rules). In the context of chemical risk assessment, the information on chemicals provided by (Q)SARs and related estimation methods, collectively referred to as ¿non-test methods¿, can be used in combination with information from test methods by applying stepwise and/or weight-of-evidence approaches in the context of integrated (or intelligent) testing strategies (Chapter 11). This chapter provides an overview of currently available (Q)SARs and expert systems for predicting human health and ecotoxicological endpoints, and supplements the review on estimation methods for physicochemical properties and fate parameters (Chapter 9). The review is preceded by an explanation of how (Q)SARs are developed and validated, and is followed by an explanation of how the models can be applied for regulatory purposes.
		},
	title = {Predicting Toxicological and Ecotoxicological Endpoints },
	url = {http://www.springer.com/environment/environmental+chemistry/book/978-1-4020-6101-1},
	volume = {},
	number = {},
	journal = {},
	pages = {427-465},
	issn = {},
	publisher = {Springer},