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Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing

机译:开发神经毒性检测的本体驱动无动物检测策略的设计与验证

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Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mechanisms of action. We give an overview of fundamental processes in neural tube formation, brain development and neural specification, aiming at illustrating complexity rather than comprehensiveness. We also give a flavor of the wealth of alternative methods in this area. Given the impressive progress in mechanistic knowledge of human biology and toxicology, the time is right for a conceptual approach for designing testing strategies that cover the integral mechanistic landscape of developmental neurotoxicity. The ontology approach provides a framework for defining this landscape, upon which an integralin silicomodel for predicting toxicity can be built. It subsequently directs the selection ofin vitroassays for rate-limiting events in the biological network, to feed parameter tuning in the model, leading to prediction of the toxicological outcome. Validation of such models requires primary attention to coverage of the biological domain, rather than classical predictive value of individual tests. Proofs of concept for such an approach are already available. The challenge is in mining modern biology, toxicology and chemical information to feed intelligent designs, which will define testing strategies for neurodevelopmental toxicity testing.
机译:发育神经毒性需要毒理学中最复杂的区域之一。动物研究只提供了对人类相关的有限信息。多年来已经开发了多种替代模型,提供了洞察力的行动机制。我们概述了神经管形成,脑发育和神经规范的基本过程,旨在说明复杂性而不是全面性。我们还提供了这一领域的替代方法的丰富味道。鉴于人体生物学和毒理学的机械知识中的令人印象深刻的进展,时间适用于设计测试策略的概念方法,涵盖发育神经毒性的整体机制景观。本体方法提供了一种用于定义这种景观的框架,可以建立用于预测毒性的积分硅司。它随后将vitroassays的选择指导在生物网络中的速率限制事件,以在模型中喂养参数调整,导致毒理学结果预测。这些模型的验证需要主要关注生物结构域的覆盖,而不是个人测试的经典预测值。这种方法的概念证明已经可用。挑战是采矿现代生物学,毒理学和化学信息,以喂养智能设计,这将定义神经发育毒性测试的测试策略。

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