Abstract

Purpose

Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed.

Materials and methods

Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets.

Results

Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from ${94}_{-5}^{+5}$ to ${192}_{-18}^{+15}$ keV/µm.

Conclusion

Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of ‘deposition of energy’ is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an ‘all hazards’ approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.

中文翻译：

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肺癌辐射相关不良结果通路的量化方法面临的挑战。

摘要

目的

不良结果通路 (AOP) 提供了一个模块化框架，用于描述跨生物组织水平的生物关键事件 (KE) 和关键事件关系 (KER) 的序列。跨 KER 的经验证据可以支持量化 AOP (qAOP) 的构建。使用从电离辐射到 DNA 导致肺癌发病率的能量沉积的示例 AOP，我们研究了量化来自所有类型压力源支持的 KER 数据的可行性。讨论了这个过程在 AOP 构建的背景下的优点和挑战。

材料和方法

对来自 AOP 的四个 KER 的剂量反应研究的经验证据进行了独立编译以进行量化。三个上游 KER 包含来自符合 AOP 指南的各种辐射类型的证据。对于这三个 KER，对来自 α 粒子研究的数据进行了重点分析，以更好地描述氡气压力源的不良结果 (AO) 过程。从表格和图形中提取数字信息以绘制和制表 KE 的响应。为了补充 AOP 量化过程的区域，在 TOPAS-nBio 中进行了蒙特卡罗 (MC) 模拟，以模拟与 AO 相关的暴露条件，例如具有分泌细胞核目标的肺支气管隔室。

结果

AOP KER 的量化强调了在 AOP 设计的压力源不可知意图下辐射类型的相关性，从而激发了对特定类型的关注。对于给定的类型，KE 响应的显着差异表明缺乏从 MIE 到 AO 关联的有意义的数据，并且需要更好的响应-响应重点研究。MC 研究估计了示例肺隔室的分泌细胞核中氡 222 及其后代发射的 α 粒子的线性能量转移 (LET) 范围从${94}_{-5}^{+5}$ 到 ${192}_{-18}^{+15}$ keV/μm。

结论

量化 AOP 组件提供了一种在不同研究中收集经验证据的方法。这突出了在使用不同辐射类型检查类似终点的研究中面临的挑战。将 KER 与“能量沉积”的 MIE 联系起来的数据被证明与 AOP 设计的压力源不可知原则不兼容。将数据限制为描述相邻 KER 之间的响应-响应关系的数据可能会更好地描述与驱动通往下一个 KE 的路径的损害相关的研究，并且仍然支持“所有危害”的方法。此类数据仍然有限，未来在辐射领域的调查可能会在设计实验和报告其结果和结果时考虑这种方法。