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首页> 外文期刊>Regulatory Toxicology and Pharmacology: RTP >Towards optimization of chemical testing under REACH: a Bayesian network approach to Integrated Testing Strategies.
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Towards optimization of chemical testing under REACH: a Bayesian network approach to Integrated Testing Strategies.

机译：致力于在REACH下优化化学测试：贝叶斯网络综合测试策略方法。

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摘要

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.
机译：综合测试策略（ITS）被认为是指导有关化学危害和风险管理的资源高效决策的工具。 ITS的发展起源于90年代中期，目的是将毒性试验中的动物使用量减少到最低限度，但它的开发仍然缺乏一个方法学上一致的框架来整合所有相关信息，更新和减少试验阶段的不确定性以及处理有条件依赖的证据。本文提出了改善ITS发展的概念和方法论建议。我们讨论了当前ITS方法的方法学缺陷，并确定了ITS开发和优化的概念要求。首先，ITS开发应基于概率方法，以便量化和更新测试阶段的各种不确定性。其次，推理应反映出一套逻辑规则，用于一致地组合相关事件的概率。第三，推理应该是假设驱动的，并且应该反映因果关系，以便在测试阶段一致地指导决策制定。为了满足这些要求，我们提出了一种信息理论的ITS开发方法，即“ ITS推断框架”，可以通过使用贝叶斯网络使之可行。作为说明，我们检查了一个简单的两次测试电池来评估啮齿动物的致癌性。最后，我们证明了运行贝叶斯网络如何揭示证据权重的定量度量。

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