Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 401090
Title Towards optimization of chemical testing under REACH: A Bayesian network approach to Integrated Testing Strategies
Author(s) Jaworska, J.; Gabbert, S.G.M.; Aldenberg, T.
Source Regulatory Toxicology and Pharmacology 57 (2010)2-3. - ISSN 0273-2300 - p. 157 - 167.
Department(s) Environmental Economics and Natural Resources Group
Aquatic Ecology and Water Quality Management
Publication type Refereed Article in a scientific journal
Publication year 2010
Keyword(s) evidence-based toxicology - alternative methods - risk-assessment - conditional dependence - decision-support - diagnostic-tests - specificity - sensitivity - prediction - batteries
Abstract Integrated Testing Strategies (ITSs) are considered tools for guiding resource efficient decision-making on chemical hazard and risk management. Originating in the mid-nineties from research initiatives on minimizing animal use in toxicity testing, ITS development still lacks a methodologically consistent framework for incorporating all relevant information, for updating and reducing uncertainty across testing stages, and for handling conditionally dependent evidence. This paper presents a conceptual and methodological proposal for improving ITS development. We discuss methodological shortcomings of current ITS approaches, and we identify conceptual requirements for ITS development and optimization. First, ITS development should be based on probabilistic methods in order to quantify and update various uncertainties across testing stages. Second, reasoning should reflect a set of logic rules for consistently combining probabilities of related events. Third, inference should be hypothesis-driven and should reflect causal relationships in order to coherently guide decision-making across testing stages. To meet these requirements, we propose an information-theoretic approach to ITS development, the “ITS inference framework”, which can be made operational by using Bayesian networks. As an illustration, we examine a simple two-test battery for assessing rodent carcinogenicity. Finally, we demonstrate how running the Bayesian network reveals a quantitative measure of Weight-of-Evidence
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