Skip to main content
NCBI home page
EPA Author Manuscripts logoLink to EPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Jan 20.
Published in final edited form as: Int J Radiat Biol. 2021 Jan 20;97(6):815–823. doi: 10.1080/09553002.2020.1857456

Collaborative Efforts Are Needed Among the Scientific Community to Advance the Adverse Outcome Pathway Concept in Areas of Radiation Risk Assessment

Vinita Chauhan 1, Daniel Villeneuve 2, Donald Cool 3
PMCID: PMC8312481  NIHMSID: NIHMS1724322  PMID: 33253609

Abstract

Background: 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 Organisation 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.

Keywords: Adverse outcome pathway framework, radiation risk assessment, key events, adverse outcome, low dose

Adverse outcome pathways

In recent years, there has been much discussion on the adverse outcome pathway (AOP) method (Ankley et al., 2012, OECD, 2016a). This approach is being used in chemical toxicology to help support safety assessments of chemicals using in vitro methods thereby increasing efficiency, reducing animal testing and improving prediction uncertainties (Villeneuve et al., 2014). AOPs are a means of combining and organizing existing mechanistic information into a measurable cascade of critical key events (KEs) using well-defined test methods that are relevant to an adverse outcome (AO) of importance to regulatory decision-making (Figure 1). They are depicted in a unidirectional and sequential manner intended to reflect a causal chain of events that connect the initial perturbation of a biological system by a stressor to a negative health impact at the individual or population level. The AOP generally begins with a molecular initiating event (MIE) that represents the first physical interaction of the stressor with a cell. The MIE then causes downstream KEs that are biologically dominant and essential to achieve the AO. The KEs do not necessarily capture every detail of the biological response to the stressor, but rather represent critical check-points or milestones along the path to the adverse outcome. Each KE is measurable/verifiable and may be evaluated through in vitro, in silico, in vivo experimental methods and KEs typically increase in level of complexity from the molecular, cellular, tissue, organ, individual to population level (OECD, 2016b). This systems approach to biology allows multidimensional delineation of adverse effects that can account for sensitivities across the different levels of organization.

Figure 1:

Figure 1:

Open in a new tab

The steps to building an adverse outcome pathway across levels of biological organization using prior knowledge from studies that support the Bradford-Hill considerations (e.g., dose-concordance shown). KE, Key event; KER, Key event relationship; AO, adverse outcome; MIE, molecular initiating event. Non-adjacent KER refers to those relationships that are not directly adjacent to each other in the sequence of the AOP but for which there may be available empirical linkages or other supporting evidence that are relevant to capture using the modular AOP framework.

Between KEs are key event relationships (KERs) that can be adjacent or non-adjacent. Key event relationships are represented by an arrow linking two KEs, one upstream that causes the second (downstream) to occur. Each KER can be conceived as a biological “if, then” statement; if KE1 is observed, KE2 can be expected. Documentation of the KER lays out the scientific basis for this “cause-effect” or “if-then” relationship (Becker et al. 2015; Figure 2). Biological plausibility describes the structural or functional relationship between the upstream event and downstream event, under normal biological conditions. Empirical support refers to evidence from experiments that show when KE1 (upstream) is perturbed there may also be an effect on KE2 (downstream) and that evidence is consistent with a causal relationship between 1 and 2: 1 is affected before an impact on 2 is observed (temporal concordance); 1 is affected at concentrations or levels of exposure less than or equal to those that affect 2 (dose concordance); if considering a population, a greater or equal proportion of the population exhibits effects consistent with KE1 than KE2 (incidence concordance). Key events in an AOP are regarded to be “essential” in that if the upstream KE1 does not occur, KE2 will not be observed (unless impacted through some other pathway). KE2 is dependent on KE1. Experimental evidence demonstrating that dependency is referred to as evidence for “essentiality”. Finally, where possible quantitative understanding of the relationship is defined by a mathematical function or model that describes how much change in KE1, over what time frame, and under what conditions, impacts the state of KE2 (Wittwehr et al. 2017). Collectively, this assembled knowledge facilitates the qualitative or quantitative extrapolation from the upstream event to an inferred/expected impact on the downstream event, and as such serves as the foundation for application of the AOP.

Figure 2:

Figure 2:

Open in a new tab

List of criteria and guiding questions that form the Bradford-Hill Consideration as modified for AOP development (Becker et al., 2015). KE, Key event. The arrow signifies the criteria that forms the strongest to weakest weight of evidence.

The successful construction of AOPs is directly related to the accessibility of relevant information from various studies that support each KER. In this way, AOPs organize currently available biological and epidemiological knowledge from the molecular to the individual level, helping to manage data and improve the understanding of potential health/fitness outcomes, which could lead to better informed decisions. AOPs are also built in a modular manner using data collected from all types of hazards, the merits of which is that the stressor is only important for defining molecular initiation. Events downstream of initiation are no longer dependent on the stressor but rather the biology of disease progression. Thus, the biological relationships described can be applied to a wide range of stressors.

The process of building evidence to support an entire AOP, or even individual KERs, is a challenge requiring extensive literature searches and the organization of the data in a manner that facilitates evaluation of the Bradford-Hill considerations (Becker et al., 2015). However, by focusing the assembly and evaluation of evidence on pair-wise associations between upstream and downstream KEs, evidence from a wide range of sources (e.g., experiments with different types of stressors, cell types and methodologies) can be organized and considered. This then provides robust evidence for each KER, demonstrating a generalized pattern of response.

Recently, a case-example of a radiation-relevant AOP for lung cancer used direct deposition of energy as the MIE (https://aopwiki.org/aops/272; Chauhan et al. 2019, 2020). A series of adjacent KERs lay out the well understood, biologically plausible mechanisms through which deposition of energy into lung tissue can lead to cancer. The adjacent KERs are flanked by non-adjacent KERs that capture the strong weight of empirical support showing patterns consistent with a causal relationship between energy deposition and measurable events like mutations, chromosome aberrations, and lung cancer incidence. The overall AOP spans multiple levels of organization from initial DNA damage in the cell, to the collective impact of unregulated cell proliferation in the tissue, to the ultimate pathology associated with the tumor, and the associated range of time-scales.

Other AOPs have included evidence that allows for quantification of the KERs. Compilation of this quantitative understanding in the form of functions or models that define the response-response relationship, time-scale of transition, known modulating factors, and feedback relationships that influence the overall complexity of dose-response and time-course behaviors can support the prediction of risk and also, perhaps more importantly, improve the identification of gaps in knowledge (Wittwehr et al., 2017; Conolly et al. 2017). Recent work on an oxidative stress AOP leading to kidney toxicity used three approaches to quantify the AOP development: empirical dose-response modeling, Bayesian network calibration and systems biology modeling. It was shown that a combination of system biology and empirical evidence was needed to gain proper perspective on the quantification process (Zgheib et al., 2019).

The Organisation for Economic Cooperation and Development (OECD) has formalized a program that supports the construction of AOPs in a transparent manner that applies modified Bradford-Hill considerations (Figure 2). With growing interest in AOP development, there is now more detailed information describing the process, on-line training courses, frequent webinars and sub-groups to manage ongoing challenges (www.aopwiki.org). The progression of building an AOP is systematic and organizers of the program are committed to ensuring AOP developers are well supported through this process resulting in the production of high quality AOPs with an intended application(s). Currently there are over 250 AOPs in development or complete, and more under way, and the framework is rapidly expanding to form entire networks of AOPs (i.e., connecting AOPs through shared modules; Knapen et al. 2018; Pollesch et al. 2019). This network context is important as it is well understood that, like other stressors, radiation can activate multiple pathways that interact in complex ways to cause adverse effects (Villeneuve et al. 2014; Knapen et al. 2018; Helm and Rudel 2020). As additional AOPs are assembled, added, and linked to one another in the knowledgebase, measurable early predicators of disease and their ability to understand their potential interactions in a complex systems context are emerging. On-going development of this framework could eventually provide an expanded accessible picture of the critical biology associated with disease culmination.

AOPs can be continuously revised and updated as the science evolves. They are intended as living documents that can be developed through crowdsourcing but must also be appropriately curated by experts to maintain a coherent and appropriate format. Given the large number of studies containing the data necessary to support the development of an AOP, this process is intensive and time consuming. It requires distribution of resources and encourages input from experts. The management of information is facilitated by the AOP knowledge base (AOP-KB, https://aopkb.org), which acts as the portal to several modules that serve as repositories for the development of AOP networks. Among these the AOP-Wiki (aopwiki.org) provides the collaborative platform upon which AOPs are documented and developed.

How can AOPs support the radiation field?

The AOP framework could be usefully applied to strengthen areas in radiation risk assessment; below are examples of some aspects that could be targeted.

Refine risk assessment for co-exposure scenarios and non-cancer outcomes.

The linear-no-threshold (LNT) model has formed the basis of radiation protection for over 50 years; it has helped define limits of exposure to the public from sources of ionizing radiation, whether manmade or environmental (Boice, 2017). The model assumes that even the smallest dose of radiation could lead to cancer induction, however, considerable uncertainty and controversy surrounds the scientific basis for this model, specifically at low doses. It extrapolates risk based on data derived from cancers induced at acute high doses and studies have not been able to determine whether this extrapolation reflects risks from chronic lower doses. Although there is a clear understanding of biological effects from radiation exposures (internal, external) in isolation, at acute high doses and in a short period of time (minutes to few days), our understanding in combination with other stressors and for non-cancer effects such as cognitive disorders, and circulatory diseases remains uncertain (NCRP, 2020). This is particularly true at chronic low doses, where other mechanisms not related to DNA damage response may be involved in the risk estimate. In this context, AOPs may have utility. Co-exposure scenarios from multiple stressors could be modeled more effectively in an AOP network. The approach can help to identify shared KEs/KERs and where delineations occur. For example, mechanisms of lung cancer induction from radon gas and cigarette smoke have identified discrete pathways. Studies have shown that radiation exposure activates receptor-mutant genes, notably EGFR while particulate matter from smoking initiates transducer mutations (KRAS) (Castellitti et al., 2019). Organizing the data within an AOP, can help define distinctions and interactions more readily and the critical KEs can inform parameters in radiation biologically based models for risk estimation (NCRP, 2020). The AOP approach is also valuable in assembling data surrounding non-cancer effects examples such as cataracts, kidney toxicity and circulatory diseases which are of interest to the radiation field. Mechanistic knowledge in these areas could then be used to guide risk assessments.

Quantitative understanding of disease progression:

Different types of radiation may produce qualitatively similar key events, that may have different paths and probabilities to the progression of disease. For example, a low linear energy transfer (LET) radiation insult from X-ray exposure could have an increased likelihood of reducing oxidative stress (KE) by being quenched by antioxidants before reaching vital macromolecules (DNA, lipids nuclear membranes) and causing damage, as compared to a high LET exposure. Similarly, exposure to a single stressor may not initiate a path to disease but in combination with other stressors a pathway may be initiated as the ability to efficiently resolve the damage could be compromised or the antioxidant capacity of the cell may be overwhelmed, thus triggering the next critical event. Therefore, a quantitative understanding of shared KEs across multiple stressors may be used to better predict probability of disease outcomes. Such an approach, has shown promise in the chemical field to identify the critical amount of damage or toxicant level below which existing mechanisms seem to effectively respond to the challenge, vs. levels that are more likely to drive progression to the next event or check-point in the pathway (i.e., key event B). Capturing this type of information can guide the development of risk-models to address questions on shape of the response kinetics at the KE level, i.e. whether KEs are governed by a threshold, or continuous damage incurs without a threshold as a result of additive or synergistic processes. Similar to biologically-based concentration response models (Kaiser et al. 2020ab), quantitative AOPs can additionally support an understanding of how shared KERs across multiple stressors can increase the probability of health outcomes and conversely what countermeasures can reduce this toxicity.

Identifying research knowledge gaps/uncertainties:

Every stressor/insult, once interacting with or entering the body, can induce a series of steps that may either induce toxicity or be effectively controlled with no ultimate health implications. This can be dependent on phenomena’s such as the cells hypersensitivity, background biological burden, and adaptive abilities, which can be difficult to detect consistently. The AOP concept links stressor-initiations and KEs to disease in a simplistic unidirectional path which can then be networked to other AOPs with the addition of feedback loops to better represent the biology to disease progression. In reducing the biological “noise” (i.e. transient/reversible and inconsistent events) it establishes dominant defined linkages to AOs. Pathways that have not yet been directly linked to disease progression which occur may then be excluded from consideration in the risk assessment process but highlighted for future focused research. For example, acute effects can involve transient events (i.e. single strand DNA breaks) that are normally reversible and resolve spontaneously. In contrast, latent delayed effects may lead to the expression of KEs (i.e. mutations) which are progressive and may or may not be irreversible. The AOP approach focuses on those KEs for which there is a causal linkage to adverse outcomes, not just a correlative association with exposure to the stressor. The focus on causal relationships also simplifies the scientific challenge from one that seeks to understand and characterize the entire complexity of biological response to hazardous exposures to a subset of biological responses that are measurable and disease centric. As knowledge is organized to sketch out and support these causal paths, critical gaps in our scientific knowledge are identified (i.e. genomic instability). Such gaps and uncertainties can be difficult to pin-point through a less systematic and targeted look at the broad and widely dispersed body of scientific literature. These knowledge gaps can then be reflected on and addressed to help fully evolve our current system of radiation protection.

Screening tools/ test gaps:

The AOP approach provides a database of measurable KEs that captures critical biological processes. Together, these can be developed into screening/biomonitoring tools that could then collectively better address questions on the dose of radiation an individual has been exposed to (biodosimetry), and those more vulnerable to radiotherapy treatments (radiation sensitivity). For example, occupational workers that perform interventional medical fluoroscopy procedures have been shown to have higher risk of cataracts (Stahl et al., 2016). Organizing current knowledge in this area could identify the most relevant measurable KEs that could guide the development of tools for early disease detection using biological markers. The AOP database could also be used to identify stressors to which an individual may have already been exposed and areas where improved testing methods are required. Helm et al., (2020) demonstrated this through the development of an AOP to breast cancer. Their work highlighted how improved/standardized assays were needed to measure key events of chronic inflammation, genomic instability and oxidative stress particularly in mammary tissue.

Individualized risk assessment

As confidence in the content within the AOP framework evolves, the wealth of information assembled and synthesized within KEs/KER descriptions could possibly support an understanding of an individual’s risk to disease that is dependent on their lifestyle, past exposures, health status, confounders and the toxicants they may have been exposed to during their life-time (Jeong et al., 2019). Although this is a long-term vision, recent efforts in the area of personalized medicine and biomarker identification have shown potential of evolving the framework towards this direction. The 1000 Genomes project allows characterization of the broad spectrum of genetic variation, and provides a benchmark for construction of risk classifiers based on health status (Auton et al., 2017). For example, recent work has shown the potential for prediction of disease based on mutational analysis in healthy individuals (Abelson et al., 2018). Other studies have also used available biological information in the area of genomics and highlighted how it can be integrated into AOPs using machine learning tools (Pittman et al. 2018). In this way, identified KEs within simple AOPs that are inexpensive and relatively easy to measure could be developed into “omic-based” microfluidic based chips which could be screened in biofluids such as blood. Inevitably, these assays will need to be sensitive, specific, easily measured, inexpensive, high throughput, and reproducible. By developing “omic”-AOP-based tools, there could be the possibility to refine risk assessment to susceptible and sensitive populations (Mortensen et al., 2018).

One hypothetical method could be to use an approach similar to allergen testing. Harvested blood from individuals could be exposed ex-vivo to multiple stressors across a range of doses and assayed for responses related to select AOPs. The range of responses could be pre-defined using both a healthy and unhealthy population. Data interpretation would then involve using modelling (e.g., benchmark dose modeling) to interpolate the relationship of the dose-thresholds and shape of the dose-response curve across the screened stressors to derive a biological response factor (BRF) that would be indicative of an individual’s efficiency to handle external stressors, and possibly be representative of their current health status (Figure 3). This BRF could be used in conjunction with current risk models to help refine risk estimates at the individual level. It could be used to help address questions on how individuals respond to specific stressors, e.g. those more vulnerable to disease and possible mitigation strategies to ensure disease prevention. As a case in point, the dose response of the CLIP2 biomarker for radiation-induced thyroid cancer can be interpreted as a BRF (Selmansberger et al. 2015). For risk assessment of radiation-induced thyroid cancer Kaiser et al. 2020b present an approach to improve the calculation of the probability of causation (POC). The POC can help to clarify the inference of the radiogenic origin of a thyroid cancer from exposure during childhood after the accidents in Chernobyl and Fukushima. Molecular information on the status of a radiation marker could be used in combination with a mechanistic model to better inform the POC. This approach in combination with AOP knowledge base represents a step forward to more personalized risk assessment.

Figure 3:

Figure 3:

Open in a new tab

Hypothetical approach to identify a biological response factor that can be used to refine risk assessments at the individual level. An individuals blood can be screened for their ability tohandle stressors using a microfluidic AOP gene-based chip. The graphs are illustrative examples and can be represented by other type of dose-response relationships.

Limitations of the AOP approach

Although the AOP approach offers opportunities to re-organize existing and new data in a way that provides a systematic method of linking early biological perturbations to potential hazards, challenges exist. The AOP approach may be overly reductionist to adequately capture radiation biology particularly in the context of systemic effects (i.e. inflammation) and influencers of metabolic processes such as the microbiome, a diversified flora of bacteria, that is symbiotically communicating with the host organ systems (Turnbaugh et al., 2007). A question that can be asked is whether these interactions be represented as independent AOPs or networked to host organ-level pathways at multiple levels of biological organization. In addition, questions remain on how best to represent the complexities of bystander effects, and hypersensitivity/hormesis response in an AOP. As yet there is not a strong weight of evidence surrounding these concepts, and it is unclear how to incorporate them within a representation of disease progression. For example, bystander effects resulting from a high dose of radiation to a targeted cell can lead to downstream KEs which indeed may not progress to disease but rather act as molecular initiators (e.g. cytokine release leading to receptor binding) to other chain of events that will need to be considered through an evaluation of AOP networks (Knapen et al. 2018).

Other challenges surround acquisition of the data used to support an AOP, which must allow for the causal linkages of KEs, and a means to accurately determine connectivity to an AO. These can be influenced by a range of factors such as time-point and experimental design at which KEs are measured and also differences in the sensitivity of the assays used to measure the two KEs. Therefore, available data must confidently identify robust concordance. Although qualitative AOPs are useful, another relevant area of value is the development of quantifiable AOPs. Confidence in the quantitative associations and their generalizability often requires pooling data across multiple stressors (e.g. radiation types), models and measured endpoints. The difficulty around this is the variation of data across studies, even those using similar assays. Although there is a vast amount of biological information, it is dispersed over multiple formats, complicating data interpretation (Pittman et al., 2018).

The mechanics of building an AOP is also time-consuming and requires considerable resources from training, formulation, and review. The assembly of knowledge using the Bradford Hill criteria may, in certain cases, be overly stringent and preventing initial identification of qualitative relationships. There is also considerable inconsistency in observations, particularly where multiple pathways are active. For example, breast cancer responses to ionizing radiation vary depending on presence or absence of hormonally-induced cell proliferation (Helm and Rudel 2020). In addition, as radiation is a stochastic insult, it may con-currently initiate multiple pathways, which raises the question of how clear of an understanding of the biology is required for AOPs to be valuable in supporting risk assessment. These are discussions that need to be undertaken through focused workshops.

Infrastructure to implement the AOP approach

To effectively implement an AOP approach, a harmonized research framework may be of value, specifically one that defines the high-level priority questions using the AOP knowledge gaps and provides guidelines for experimental design to data reporting (Figure 4a). This will allow for the development of AOP formulated studies that span across levels of biological organizations which may provide the high-content data needed to link the bioactivity of pathway activation to toxicity and inform on the linkages to human relevance using mechanistic data. Considerable work, funds, and resources are needed to produce studies with multiple causally linked KEs, from many stressors through all levels of organization to disease. This gap is best addressed through collaborative efforts using a systematic approach from project design to output and integration. Coordination and collaboration efforts are already underway in several organizations advocating for consideration of the AOP approach for radiation risks (Canada, United States, Europe). Under the International Dose Effect Alliance, workshops have examined the approach, and a working group is considering how to facilitate coordination and support for development of AOP’s. Through OECD/Nuclear Energy Agency, a High Level Group for Low Dose Research has been formed to bring together funding organizations and researchers. Increased connections with the OECD Extended Advisory Group for Molecular Screening and Toxicogenomics (EAGMST) and chemical AOP community have begun with a Webinar in July, 2020 and follow-on workshops in subsequent months/years.

Figure 4:

Figure 4:

Open in a new tab

A) Steps to the development of a harmonized research framework that incorporates the adverse outcome pathway (AOP). B. Steps to integration of the AOP concept through collaborations with the chemical community and partnering to develop proposals.

Promotion, and knowledge transfer will also be valuable to enable effective AOP uptake (Figure 4b). Engagement of societies and journals may be a first step towards education, possibly facilitated by survey styled scanning exercises and through the journal submission process (Figure 5). Authors of accepted manuscripts with experimental data that meet AOP convention could be invited by journal editors to submit their data to the AOP Wiki (www.aopwiki.org). Curating and organizing newly available data as they are generated, rather than the current ad hoc and retrospective manner, could facilitate progression in AOP development and show its broader utility to the scientific community. It could also reduce the burden of developing AOPs, a process that is currently quite time-consuming and labor-intensive due to the requirement of searching and reviewing the literature, extracting the data and inputting it appropriately into the AOP-Wiki. Approaches for automating this process would be a significant stride toward broader uptake of the concept. Long-term this might allow for more focused development of collaborative projects and co-ordination of research across institutions.

Figure 5:

Figure 5:

Open in a new tab

A vision for promoting the AOP concept and AOP development through radiation journal submission process.

Conclusions

Refining risk assessments will require the radiation community to deliberate on how existing data can be integrated to meaningful outputs. Although, we have better techniques and more data in the low dose region to help understand risk, considerable uncertainty remains. A first step towards reducing this uncertainty is better curation, organization, and synthesis of scientific results into actionable knowledge concerning probable toxicological effects and associated means to understand the susceptibility of populations or individuals. This could be achieved, at least in part, through organization and critical evaluation of existing and new data using the AOP framework and providing an expandable and living knowledge base that would capture the current state of knowledge and direct future work. This undertaking will require co-operation, co-ordination and vetting of research. There have already been significant investments in advancement of AOPs by the chemical safety community, from both a human health and ecological perspective. Currently, the radiation community is moving in a similar direction. Together, through organized international activities, productive dialogue between research scientists and regulatory communities across disciplines can begin allowing for the expansion of the AOP approach. Long-term this will allow for better co-ordination of research, standardized reporting of data, and impactful outcomes that can address diverse exposure scenarios and broad priority areas, helping to evolve radiation risk assessment.

Acknowledgements:

The authors are grateful to Ruth Wilkins, Carole Yauk, Sami Qutob and Holly Mortensen for critical review of the manuscript. The contents of this manuscript neither constitute, nor necessarily reflect official US EPA policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. The views and thoughts in this manuscript are not intended to represent those of the Electric Power Research Institute (EPRI) and do not imply endorsement by EPRI.

Biographical Note:

VC is a research scientist at Health Canada. DV is a research toxicologist with the US Environmental Protection Agency. DC is a technical executive with Electrical Power Research Institute, Charlotte, NC.

Footnotes

Conflict of Interest: The authors declare no conflict of interest.

Prepared for special issue of Low dose biology, epidemiology, its integration and implications for radiation protection: an update.

References

  1. Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, Mount DR, Nichols JW, Russom CL, Schmieder PK, et al. (2010). Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem 29, 730–741. [DOI] [PubMed] [Google Scholar]
  2. Villeneuve DL, Crump D, Garcia-Reyero N, Hecker M, Hutchinson TH,LaLone CA, Landesmann B, Lettieri T, Munn S, Nepelska M, Ottinger MAL, Vergauwen L, Whelan M. (2014) Adverse outcome pathway (AOP) development I: strategies and principles, Toxicol. Sci 142 312–320. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Becker RA, Ankley GT, Edwards SW, Kennedy SW, Linkov I, Meek B, Sachana M, Segner H, Van Der Burg B, Villeneuve DL (2015). Increasing scientific confidencein adverse outcome pathways: Application of tailored Bradford-Hill considerations for evaluating weight of evidence. Regul. Toxicol. Pharmacol 72, 514–537. [DOI] [PubMed] [Google Scholar]
  4. Boice JD Jr (2017) The linear nonthreshold (LNT) model as used in radiation protection: an NCRP update. Int J Radiat Biol 93:1079–1092. [DOI] [PubMed] [Google Scholar]
  5. Castelletti N, Kaiser JC, Simonetto C, Furukawa K, Küchenhoff H, Stathopoulos GT. (2019) Risk of lung adenocarcinoma from smoking and radiation arises in distinct molecular pathways. Carcinogenesis October 16;40(10):1240–1250. [DOI] [PubMed] [Google Scholar]
  6. Chauhan V, Said Z, Daka J, Sadi B, Bijlani D, Marchetti F, Beaton D, Gaw A, Li C, Burtt J, Leblanc J, Desrosiers M, Stuart M, Brossard M, Vuong NQ, Wilkins R, Qutob S, McNamee J, Wang Y, Yauk C. (2019) Is there a role for the adverse outcome pathway framework to support radiation protection? Int J Radiat Biol. February;95(2):225–232. [DOI] [PubMed] [Google Scholar]
  7. Chauhan V, Stricklin D, Cool D. (2020) The Integration of the Adverse Outcome Pathway Framework to Radiation Risk Assessment. Int J Radiat Biol. 2020;1–21. [DOI] [PubMed] [Google Scholar]
  8. Conolly RB, Ankley GT, Cheng W, Mayo ML, Miller DH, Perkins EJ, Villeneuve DL, & Watanabe KH (2017). Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology. Environmental science & technology, 51(8), 4661–4672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Helm JS, Rudel RA Adverse outcome pathways for ionizing radiation and breast cancer involve direct and indirect DNA damage, oxidative stress, inflammation, genomic instability, and interaction with hormonal regulation of the breast. Arch Toxicol 94, 1511–1549 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Jeong J, Garcia-Reyero N, Burgoon L, Perkins E, Park T, Kim C, Roh JY, & Choi J (2019). Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach. Chemical research in toxicology, 32(6), 1212–1222. [DOI] [PubMed] [Google Scholar]
  11. Kaiser JC, Blettner M, Stathopoulos GT. Biologically based models of cancer risk in radiation research. Int J Radiat Biol. 2020. July 16:1–10. [DOI] [PubMed] [Google Scholar]
  12. Kaiser JC, Misumi M, Furukawa K. (2020) Biologically-based modeling of radiation risk and biomarker prevalence for papillary thyroid cancer in Japanese a-bomb survivors 1958–2005. Int J Radiat Biol. July 2:1–12. [DOI] [PubMed] [Google Scholar]
  13. Knapen D, Angrish MM, Fortin MC, Katsiadaki I, Leonard M, Margiotta-Casaluci L, Munn S, O’Brien JM, Pollesch N, Smith LC, Zhang X, & Villeneuve DL (2018). Adverse outcome pathway networks I: Development and applications. Environmental toxicology and chemistry, 37(6), 1723–1733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. LaLone CA, Ankley GT, Belanger SE, Embry MR, Hodges G, Knapen D, Munn S, Perkins EJ, Rudd MA, Villeneuve DL, Whelan M, Willett C, Zhang X, Hecker M. (2017) Advancing the adverse outcome pathway framework-An international horizon scanning approach. Environ Toxicol Chem. June;36(6):1411–1421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Mortensen HM, Chamberlin J, Joubert B, Angrish M, Sipes N, Lee JS, & Euling SY (2018). Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment. Mammalian genome: official journal of the International Mammalian Genome Society, 29(1–2), 190–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. NCRP (2020). Approaches for Integrating Information from Radiation Biology and Epidemiology to Enhance Low-Dose Health Risk Assessment, NCRP Report No. 186, National Council on Radiation Protection and Measurements, 2020. [Google Scholar]
  17. NCRP (2018). Implications of Recent Epidemiologic Studies for the Linear-Non threshold Model and Radiation Protection, NCRP Commentary 27, National Council on Radiation Protection and Measurements, 2018. [Google Scholar]
  18. OECD (2016a) Organisation of Economic Co-operation and Development (OECD), Guidance Document for the Use of Adverse Outcome Pathways in Developing Integrated Approaches to Testing and Assessment (IATA). Series on Testing and Assessment, No. 260, ENV/JM/MONO (2016a) 67, OECD Environment, Health and Safety Publications 2016 Organisation for Paris, France; 67&doclanguage=en. [Google Scholar]
  19. OECD. (2016b). Users’ Handbook supplement to the Guidance Document for developing and assessing Adverse Outcome Pathways. Environment, Health and Safety Publications, Series on Testing and Assessment No. 233, Organisation for Economic Cooperation and Development, Paris, France. Available at: http://aopkb.org/common/AOP_Handbook.pdf; Accessed October 25, 2016. [Google Scholar]
  20. Pittman ME, Edwards SW, Ives C, & Mortensen HM (2018). AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks. Toxicology and applied pharmacology, 343, 71–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Pollesch NL, Villeneuve DL, & O’Brien JM (2019). Extracting and Benchmarking Emerging Adverse Outcome Pathway Knowledge. Toxicological sciences: an official journal of the Society of Toxicology, 168(2), 349–364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Selmansberger M, Kaiser JC, Hess J, Güthlin D, Likhtarev I, Shpak V, Tronko M, Brenner A, Abend M, Blettner M, Unger K, Jacob P, Zitzelsberger H. (2015) Dose-dependent expression of CLIP2 in post-Chernobyl papillary thyroid carcinomas. Carcinogenesis. July;36(7):748–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Stahl CM, Meisinger QC, Andre MP, Kinney TB, Newton IG. (2016) Radiation Risk to the Fluoroscopy Operator and Staff. AJR Am J Roentgenol. October;207(4):737–744. [DOI] [PubMed] [Google Scholar]
  24. Turnbaugh P, Ley R, Hamady M. Fraser-Liggett CM, and Knight R (2007). The Human Microbiome Project. Nature 449, 804–810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Wittwehr C, Aladjov H, Ankley G, Byrne HJ, de Knecht J, Heinzle E, Klambauer G, Landesmann B, Luijten M, MacKay C, Maxwell G, Meek ME, Paini A, Perkins E,Sobanski T, Villeneuve D, Waters KM, Whelan M.(2017). How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for RegulatoryToxicology. Toxicol Sci. February;155(2):326–336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Zgheib E, Gai W, Limonciel A, Aladjov H. Yang H, Tebby C, Gayraud G, Jennings P, Sachana M, Beltman JB, et al. (2019). Application of three approaches for quantitative AOP development to renal toxicity. Comp Tox. 11:1–13. [Google Scholar]

RESOURCES