Towards a quantitative adverse outcome pathway for liver carcinogenesis: From proliferation to prediction

IF 3.1 Q2 TOXICOLOGY
Christina H.J. Veltman , Hiba Khalidi , Elias Zgheib , Bob van de Water , Mirjam Luijten , Jeroen L.A. Pennings
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引用次数: 0

Abstract

Hazard assessment of non-genotoxic carcinogens could greatly benefit from next generation risk assessment approaches, driven by the multitude of mechanisms through which non-genotoxic carcinogens operate. One method for structuring new approach methodology-derived data is the adverse outcome pathway (AOP) concept. Currently, mostly qualitative AOPs are described, limiting their application for regulatory decision making. In contrast, quantitative AOPs use mathematical terms to describe the relationships between key events (KEs), allowing for the derivation of a Point of Departure (PoD). Here, we report quantification of the key event relationship (KER) between sustained hepatocyte proliferation and liver tumour formation, two KEs of AOP#220 relating to CYP2E1 activation leading to liver cancer. We use incidence of histopathological lesions indicative of proliferation, as well as BrdU labelling obtained from existing sub-chronic toxicity studies in rats, to quantify proliferation. For liver cancer, incidences of hepatocellular adenoma and carcinoma from 2-year rodent carcinogenicity studies were collected. Data for both KEs were combined to calibrate a response-response model, and Bayesian logistic regression analysis was applied to obtain predictions and credible intervals for carcinogenicity. Proliferative lesion incidence was observed to be a highly specific, yet insensitive predictor, and combining this with BrdU labelling yields more accurate predictions of carcinogenicity. Importantly, we demonstrate that for most of the chemicals tested, inclusion of BrdU labelling returns more precise predicted benchmark dose intervals for PoD derivation. To further explore this quantitative KER and its regulatory application, we propose to include and standardize BrdU labelling for sub-chronic toxicity studies performed for regulatory purposes.
肝癌发生不良后果的定量途径:从增殖到预测
非基因毒性致癌物的危害评估可以极大地受益于下一代风险评估方法,这些方法是由非基因毒性致癌物的多种作用机制驱动的。构建新方法方法衍生数据的一种方法是不良结果路径(AOP)概念。目前,对aop的描述大多是定性的,限制了它们在监管决策中的应用。相比之下,定量aop使用数学术语来描述关键事件(ke)之间的关系,从而允许推导出一个起点(PoD)。在这里,我们报告了持续肝细胞增殖和肝脏肿瘤形成之间的关键事件关系(KER)的量化,AOP#220的两个ke与CYP2E1激活导致肝癌有关。我们使用指示增殖的组织病理学病变发生率,以及从现有的大鼠亚慢性毒性研究中获得的BrdU标记来量化增殖。对于肝癌,收集了2年啮齿类动物致癌性研究中肝细胞腺瘤和肝癌的发生率。将两种ke的数据合并以校准响应-响应模型,并应用贝叶斯逻辑回归分析获得致癌性的预测和可信区间。观察到增生性病变发生率是一个高度特异性但不敏感的预测因子,将其与BrdU标记相结合可以更准确地预测致癌性。重要的是,我们证明,对于大多数被测试的化学品,包含BrdU标签可以为PoD衍生提供更精确的预测基准剂量间隔。为了进一步探索定量KER及其监管应用,我们建议将BrdU标签纳入并标准化用于监管目的的亚慢性毒性研究。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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