Cayley Alex N, Foster Robert S, Hill Emma, Kane Steven, Kocks Grace, Myden Alun, Newman Daniel, Stalford Susanne A, Vessey Jonathan D, Zarei Reza, De Oliveira Antonio Anax F
Lhasa Limited, Leeds, West Yorkshire, UK.
ALTEX. 2023;40(1):34–52. doi: 10.14573/altex.2201311. Epub 2022 May 13.
The traditional paradigm for safety assessment of chemicals for their carcinogenic potential to humans relies heavily on a battery of well-established genotoxicity tests, usually followed up by long-term, high-dose rodent studies. There are a variety of problems with this approach, not least that the rodent may not always be the best model to predict toxicity in humans. Consequently, new approach methodologies (NAMs) are being developed to replace or enhance predictions coming from the existing assays. However, a combination of the data arising from NAMs is likely to be required to improve upon the current paradigm, and consequently a framework is needed to combine evidence in a meaningful way. Adverse outcome pathways (AOPs) represent an ideal construct on which to organize this evidence. In this work, a data structure outlined previously was used to capture AOPs and evidence relating to carcinogenicity. Knowledge held within the predictive system Derek Nexus was extracted, built upon, and arranged into a coherent network containing 37 AOPs. 60 assays and 351 in silico alerts were then associated with KEs in this network, and it was brought to life by associating data and contextualizing evidence and predictions for over 13,400 compounds. Initial investigations into using the network to view knowledge and reason between evidence in different ways were made. Organizing knowledge and evidence in this way provides a flexible framework on which to carry out more consistent and meaningful carcinogenicity safety assessments in many different contexts.
传统的化学物质对人类致癌潜力的安全性评估范式严重依赖一系列成熟的遗传毒性测试，通常随后进行长期、高剂量的啮齿动物研究。这种方法存在各种问题，尤其是啮齿动物可能并不总是预测人类毒性的最佳模型。因此，正在开发新的方法学（NAMs）来取代或增强现有检测方法的预测能力。然而，可能需要结合NAMs产生的数据来改进当前范式，因此需要一个框架以有意义的方式整合证据。不良结局途径（AOPs）是组织这些证据的理想架构。在这项工作中，使用先前概述的数据结构来捕获AOPs和与致癌性相关的证据。提取了预测系统Derek Nexus中包含的知识，在此基础上进行构建，并将其整理成一个包含37个AOPs的连贯网络。然后将60种检测方法和351个计算机模拟警报与该网络中的关键事件（KEs）相关联，并通过关联数据以及将超过13400种化合物的证据和预测情境化，使该网络具有实际意义。对使用该网络以不同方式查看知识和证据之间的推理进行了初步研究。以这种方式组织知识和证据提供了一个灵活的框架，可在此框架上在许多不同背景下进行更一致且有意义 的致癌性安全性评估。
