Abstract

Disease prevention and prediction have led to the generation of phenotypically based methods for deriving the limits of safety across toxicological disciplines. In the ionizing radiation field, human data has formed the basis of the linear-no-threshold (LNT) model for risk estimates. However, uncertainties around its accuracy at low doses and low dose-rates have led to passionate debates on its effectiveness to derive radiation risk estimates under these conditions. Concerns arise from the linear extrapolation of data from high doses to low doses, below 0.1 Gy where there is considerable variability in the scientific literature. Efforts to address these controversies have led to a mountain of mechanistic data to improve the understanding of molecular and cellular effects related to phenotypic changes. These data provide fragments of information that have yet to be combined and used effectively to improve modeling, reduce uncertainties, and update radiation protection approaches. This paper suggests a better consolidation of mechanistic research may serve to guide priority research and facilitate translation to risk assessment. An effective approach that may be implemented is the organization of data using the adverse outcome pathway (AOP) framework, a programme that has been launched by the Organization for Economic Cooperation and Development in the chemical toxicology field. The AOP concept has proved beneficial to human health and ecological toxicological fields, demonstrating possibilities for better linkages of mechanistic data to phenotypic effects. A similar approach may be beneficial to the field of radiation research. However, for this to work effectively, collaborative efforts are needed among the scientific communities in the area of AOP development and documentation. Studies will need to be evaluated, re-organized and integrated into AOPs. Here, details of the AOP approach and areas it could support in the radiation field are discussed. In addition, challenges are highlighted and steps to integration are outlined. Organizing studies in this manner will facilitate a better understanding of our current knowledge in the radiation field and help identify areas where more focused work can be undertaken. This will, in turn, allow for improved linkage of mechanistic data to human relevance and better support radiation risk assessments.

 抽象的

疾病预防和预测催生了基于表型的方法的产生，用于推导跨毒理学学科的安全限度。在电离辐射领域，人类数据构成了用于风险评估的线性无阈值（LNT）模型的基础。然而，其在低剂量和低剂量率下的准确性的不确定性引发了对其在这些条件下得出辐射风险估计的有效性的激烈争论。人们对数据从高剂量到低剂量（低于 0.1 Gy）的线性外推产生了担忧，其中科学文献中存在相当大的差异。解决这些争议的努力已经产生了大量的机制数据，以提高对与表型变化相关的分子和细胞效应的理解。这些数据提供了尚未有效组合和使用的信息片段，以改进建模、减少不确定性并更新辐射防护方法。本文建议更好地整合机制研究可能有助于指导优先研究并促进转化为风险评估。可以实施的有效方法是使用不良结果途径（AOP）框架来组织数据，该框架是经济合作与发展组织在化学毒理学领域启动的一项计划。 AOP 概念已被证明有益于人类健康和生态毒理学领域，展示了机械数据与表型效应更好联系的可能性。类似的方法可能有益于辐射研究领域。 然而，为了使其有效发挥作用，科学界需要在 AOP 开发和文档领域进行协作。研究需要进行评估、重新组织并整合到 AOP 中。这里讨论了 AOP 方法的细节及其在辐射领域中可以支持的领域。此外，还强调了挑战并概述了整合步骤。以这种方式组织研究将有助于更好地了解我们当前在辐射领域的知识，并有助于确定可以开展更有针对性的工作的领域。反过来，这将改善机械数据与人类相关性的联系，并更好地支持辐射风险评估。