REVIEW

OPEN ACCESS

# Adverse Outcome Pathways Applied to Space Radiation Research

Vinita Chauhan¹ | Veronica S. Grybas¹ | Devyn Hoopfer¹ | Casey Higginson¹ | Elizabeth A. Ainsbury²,³ | Omid Azimzadeh⁴ | Afshin Beheshti⁵,⁶ | Steve Blattnig⁷ | Marjan Boerma⁸ | Sylvain V. Costes⁹ | Stephen Doty¹⁰ | Christelle Adam-Guillermin¹¹ | Nobuyuki Hamada¹² | Patricia Hinton¹³ | Janice L. Huff⁷ | Robert Reynolds¹⁴ | Ruth C. Wilkins¹ | Scott Wood¹⁵ | Carole L. Yauk¹⁶

¹Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, Canada | ²Radiation Effects Department, Radiation, Chemical and Environmental Hazards Directorate, UK Health Security Agency, Birmingham, UK | ³Environmental Research Group Within the School of Public Health, Faculty of Medicine at Imperial College of Science, Technology and Medicine, London, UK | ⁴Federal Office for Radiation Protection (BfS), Section Radiation Biology, Neuherberg, Germany | ⁵McGowan Institute for Regenerative Medicine - Center for Space Biomedicine, Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA | ⁶Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA | ⁷NASA Langley Research Center, Hampton, Virginia, USA | ⁸University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA | ⁹NASA Ames Research Center, Space Biosciences Research Branch, Mountain View, California, USA | ¹⁰Hospital for Special Surgery Research Institute, New York, New York City, USA | ¹¹PSE-SANTE/SDOS/LMDN, Institut de Radioprotection et de Sureté Nucléaire (IRSN), Provence, France | ¹²Biology and Environmental Chemistry Division, Sustainable System Research Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), Chiba, Japan | ¹³Canadian Forces Environmental Medicine Establishment, Toronto, Ontario, Canada | ¹⁴KBR, NASA Johnson Space Center, Houston, Texas, USA | ¹⁵NASA Johnson Space Center, Houston, Texas, USA | ¹⁶Department of Biology, University of Ottawa, Ottawa, Ontario, Canada

**Correspondence:** Vinita Chauhan (vinita.chauhan@hc-sc.gc.ca)

**Received:** 30 December 2024 | **Revised:** 25 May 2025 | **Accepted:** 4 August 2025

**Funding:** The work has been funded partially by the Canadian Space Agency, Health Canada Solutions Fund, and a Genomics Research and Development Initiative. Professor Ainsbury is a member of the Health Protection Research Unit in Chemical and Radiation Threats and Hazards, a partnership between UK Health Security Agency and Imperial College London, which is funded by the National Institute for Health Research (NIHR). Dr. Yauk (University of Ottawa) acknowledges that her contributions were undertaken in part thanks to funding from the Canada Research Chairs program. Drs. Huff and Blattnig are supported by the NASA Human Research Program, Space Operations Mission Directorate.

**Keywords:** adverse outcome pathway | AOP | OECD | radiation | radiation protection

## ABSTRACT

Long-duration spaceflight exposes astronauts to various stressors that can alter human physiology, potentially causing immediate and long-term health effects. These stressors can damage biomolecules, cells, tissues, and organs, leading to adverse outcomes. Developing adverse outcome pathways (AOPs) relevant to radiation exposure can guide research priorities and inform risk assessments of future space exploration activities. Through expert consultation, we developed an AOP network linking 18 key events (KEs) to four non-cancer outcomes: learning and memory impairment, bone loss, abnormal vascular remodeling, and cataract development. A novel scoping review methodology informed the evidence evaluation and supported causal linkages between two KEs. The AOP network begins with the molecular initiating event (MIE) of energy deposition onto cells, which may trigger oxidative stress and DNA damage. If DNA damage is misrepaired, it could lead to gene mutations or chromosomal aberrations. In cases where these occur in critical cell cycle genes, there is a possibility of uncontrolled cellular proliferation. Persistent KEs may contribute to the activation of tissue-resident cells, suppression of anti-inflammatory processes, and promotion of

**Accepted by:** J. O'Brien

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 His Majesty the King in Right of Canada. Crown copyright and The Author(s). *Environmental and Molecular Mutagenesis* published by Wiley Periodicals LLC on behalf of Environmental Mutagenesis and Genomics Society. Reproduced with the permission of the Minister of Health. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
chronic inflammation. This inflammatory cycle, potentially driven by mitochondrial dysfunction and immune cell activation, could lead to cell death and tissue damage. Over time, this accumulation of damage might contribute to organ-specific adverse outcomes associated with radiation exposure. This AOP network consolidates knowledge across biological levels and identifies gaps in understanding causal relationships. It aims to guide research for space traveler risk models and can also apply to other radiation exposure scenarios, such as in medical or occupational settings.

# 1 | Introduction

Space presents a complex environment for astronauts, subjecting them to multiple stressors, including radiation, microgravity or hypergravity, isolation, confinement, and altered CO₂ levels. Evaluating the risks associated with these stressors is inherently challenging. Space agencies like NASA use integrated design tools and risk models to establish safety regulations designed to minimize these risks. Among the many stressors, radiation is of particular concern. For radiation, baseline risks are assessed based on population incidence and mortality data, which are then applied to calculate the excess risk posed by space radiation. This excess risk is adjusted using specific weighting factors (Huff et al. 2022). However, a key challenge in these models is the variety of radiation types astronauts may encounter, including energetic photons, electrons, protons, helium nuclei, and high-charge, high-energy (HZE) ions, all of which can cause biological damage at doses equal to or even lower than those found at Earth's surface (Huff et al. 2022; Boerma et al. 2016).

In addition to radiation, microgravity affects biological systems, influencing blood flow, the central nervous system (CNS), circadian rhythms, and bone density (Man et al. 2022; Afshinnekoo et al. 2020). Understanding these effects in complex, mixed-exposure scenarios is particularly difficult, especially given that most astronauts have not traveled beyond Earth's magnetosphere, resulting in a lack of deep space health data (Huff et al. 2022). Compounding this issue is the lack of chronic exposure studies involving high-linear energy transfer (LET) radiation in humans, requiring extrapolation from in vivo and in vitro models, which limits the strength of predictive results (Antonsen et al. 2023).

To address some of these challenges, a framework is needed to integrate mechanistic knowledge on the health outcomes resulting from the various hazards encountered in space. The Organisation for Economic Co-operation and Development (OECD) Adverse Outcome Pathway (AOP) framework offers a structured approach for organizing scientific data into sequences of causally related events that lead to adverse health outcomes (Ankley et al. 2010; OECD 2018). An AOP begins with a molecular initiating event (MIE), followed by intermediate key events (KEs), and culminates in an adverse outcome (AO). These KEs are linked by key event relationships (KERs), with causality assessed using the modified Bradford-Hill criteria, which include biological plausibility, essentiality, and empirical evidence such as dose-, incidence-and time-concordance (Becker et al. 2015). The strength of these relationships is evaluated through a weight of evidence analysis for each KER (Villeneuve et al. 2014). Although AOPs are typically simplified to focus on measurable KEs, their linear structure allows for the development of networks where multiple MIEs can converge to produce the same AO. This flexibility makes the AOP

framework particularly effective for assessing the complex, multi-stressor environment of space (Table 1).

While AOPs have traditionally been used to describe chemical and ecological outcomes, there is growing interest in applying this framework to radiation research, particularly in the context of space (Chauhan et al. 2024; NCRP 2020; Chauhan et al. 2019). For example, the AOP for lung cancer caused by radon exposure has been endorsed by the Nuclear Energy Agency (NEA) and other OECD bodies (https://aopwiki.org/aops/272). Since 2021, the NEA's Radiation/Chemical AOP Joint Topical Group has worked to integrate AOPs into radiation research and risk assessments (Chauhan, Beaton, et al. 2022; Chauhan, Hamada, et al. 2022).

Building on these efforts, an AOP network was developed for non-cancer health outcomes relevant to space travel (Kozbenko et al. 2024; Carrothers et al. 2024; Sleiman et al. 2024; Sandhu et al. 2024). The AOs selected for this network include bone loss (https://www.aopwiki.org/aops/482), cataracts (https://www.aopwiki.org/aops/478), learning and memory impairment (https://www.aopwiki.org/aops/483), and abnormal vascular remodeling (https://www.aopwiki.org/aops/470). It is important to note that this is not an exhaustive list, and future AOP development should also address other AOs, such as reproductive effects, musculoskeletal atrophy, spaceflight-associated neuro-ocular syndrome (SANS) (Mehare et al. 2024), and cancer (Turker et al. 2017; Sasi et al. 2015; Patel et al. 2020; Kennedy et al. 2018). While cancer falls outside the scope of the current network, it is covered by other AOPs (e.g., AOP #272 (Chauhan et al. 2021; Sherman et al. 2023); AOP #432 (Klokov et al. 2022)) and could eventually be integrated into this network. The following sections outline the development process of this AOP network, summarizing key publications, highlighting knowledge gaps, and providing actionable recommendations for future research.

# 2 | Strategy for Evidence Collection for AOP Development

The strategy used for building the AOP network has been described (Kozbenko et al. 2022). Traditionally, AOPs are built using a narrative approach. However, in this initiative, elements of systematic review were incorporated and a transparent approach to data retrieval and evidence evaluation was undertaken. Briefly, the AOP workflow was divided into three phases (Figure 1). Of these, Phase I (Preliminary AOP development) involved the construction of a hypothetical AOP through a subject matter consultation and a manual literature search of ~100 studies relevant to space travel using search engines (e.g., Google Scholar and PubMed) and databases and reports provided by NASA and CSA. This initial search identified the four AOs along with 40 adjacent KERs (two consecutive KEs in an AOP) and 18 non-adjacent KERs (not sequential),
<table><caption>**TABLE 1** | Addressing space travel challenges with adverse outcome pathways (AOPs).</caption><thead><tr><td>Challenge</td><td>How AOPs address the challenge</td></tr></thead><tbody><tr><td>Complex multi-stressor environment</td><td>Space-relevant stressors (e.g., radiation, altered gravity, altered CO<sub>2</sub> levels) can be mapped to events in AOPs to analyze how they interact and impact the adverse outcomes (AOs)</td></tr><tr><td>Sparse quantitative data</td><td>By organizing data from animal and in vitro models around mechanistic endpoints, AOPs enhance extrapolation to human health risks and improve predictive accuracy. Data organized through AOPs can provide quantitative information to inform risk models for multiple AOs and aid in translating findings to acceptable dose estimates</td></tr><tr><td>Complex non-cancer outcomes</td><td>AOPs simplify the broad spectrum of pathophysiologies for non-cancer outcomes (e.g., cardiovascular disease, neurological disorders) by focusing on data-rich mechanistic endpoints, facilitating targeted research and understanding</td></tr><tr><td>Relevant experiments</td><td>AOPs provide a framework for experimental design around key mechanistic endpoints that are assessed through various initiating events to understand their impact on the severity and progression of biological effects</td></tr></tbody></table>

which were justified based on the retrieval of limited empirical evidence in the form of dose-, time-, and incidence-concordance. In Phase II (Creation of evidence retrieval protocols), a librarian performed structured literature searches across each KER. A study protocol was developed and registered (osf.io/t9amw) using established guidelines from the EVNINT PRISMA-SM-P report (Elsevier 2017). In Phase III (Weight of evidence gathering and assessment), the study protocol was tested, and amendments were made to Phase II accordingly, which resulted in three workflows describing increasingly efficient processes for AOP evidence collection (Kozbenko et al. 2022) (Figure 1). Databases generated by the librarian were screened in SWIFT Review (Sciome Workbench for Interactive computer-Facilitated Text-mining) www.sciome.com/ swift-review/ released 08.28.2019: version 1.43, which was used to prioritize studies based on a population, exposure, and outcome/ endpoint (PEO/E) statement, and the relevant data files were imported into Distiller (DistillerSR, Ontario, Canada), an artificial

intelligence (AI) based software. This method reduced the number of full-text article reviews and focused screeners' attention on prioritized search results. A PRISMA diagram depicting reference screening for all AOs is found in Figure 2. Approaching AOP development in a systematic manner and using AI along with multi-level screening allowed for less bias, increased transparency, and reduced burden on human screeners (Kozbenko et al. 2022).

### 3 | Building the AOP Network

The three-tiered screening led to the development of an initial AOP network (Figure 3). This hypothetical AOP network illustrates the sequential chain of causally linked events from an MIE to measurable KEs across levels of biological organization to non-cancer outcomes. The KEs were refined as the complete screening of the literature was undertaken. Though the principles of AOP development indicate that an AOP should be stressor-agnostic, radiation studies were prioritized, as it is among the most detrimental hazards encountered in space and of high interest in other radiation protection sectors (e.g., environmental, occupational and medical exposures). Future work could involve a complete assessment of all stressors and other AOs relevant to the space environment.

Ten out of 18 KEs (KE #155, KE #185, KE #870, KE #1392, KE #1492, KE #1493, KE #1634, KE #1635, KE #1636, KE #1686) in the AOP network were reused from existing endorsed AOPs in the AOP-Wiki (www.aopwiki.org). This includes the AO of learning and memory impairment (KE #341). The AOP network was supported by a wide range of radiation stressors and is therefore not specific to any particular exposure parameter (i.e., dose-range, dose rate, or radiation quality). This aligns with AOP principles, as AOPs are informed by the understanding of the upstream biological perturbations in the context of the downstream KEs, and these perturbations can be induced by many stressors. Some KEs are broad in scope (capture multiple biological effects) as the complexity of some molecular and cellular changes can be difficult to delineate, and parallel mechanisms may be operating in concert to cause the downstream effects. As more knowledge emerges, these KEs can be split into more focused KEs if required for specific radiation exposures. Given the often limited data available for adjacent KERs, the incorporation of non-adjacent KERs in AOPs becomes essential to address gaps. Many KEs were identified that relate to both radiation exposures and microgravity. Where appropriate, studies from both stressors were captured within the AOP evidence evaluation, particularly for KEs that are not directly linked to the MIE. Further integration of this network (e.g., to include MIEs related to other space stressors) with other relevant AOPs can advance the quantitative understanding of the impacts of space travel.

### 4 | Defining Components of the AOP

#### 4.1 | MIEs

In building the AOP, considerable discussions were directed to the selection of the initiating events for the project. Space is a complex environment where altered gravity and radiation are important contributors to astronaut health. An MIE for
<figure><img src="image_1.png" /><figcaption>**FIGURE 1** | Workflow for the phases of the project and evidence gathering to support key event relationships using the Bradford-Hill criteria.</figcaption></figure>

<figure><img src="image_2.png" /><figcaption>**FIGURE 2** | PRISMA diagram depicting reference screening of all AOs during the scoping review as described in Kozbenko et al. (2022). "Other sources" refers to additional papers supporting the AOP outside of the Medline literature searches (e.g., from subject matter experts and reference sections of review articles). References could be excluded for multiple exclusion reasons simultaneously. Literature searches for each AO may have identified the same studies.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>FIGURE 3 | AOP network of four intersecting adverse outcomes linked by multiple adjacent key events beginning with deposition of energy.</figcaption></figure>

microgravity was discussed but it was difficult to define. Future iterations of the AOP could improve this aspect. In terms of radiation, the most relevant particles to space travel are protons, neutrons, alpha particles, and heavy ions, either ejected during solar particle events (SPEs) or as components of galactic cosmic rays (GCR) (Townsend 2005; Chancellor et al. 2014). Another major concern for crew exposure is solar cosmic radiation, specifically the unpredictable occurrence of large SPEs. During a Mars mission, crew members will likely be exposed to several SPEs, and spacecraft shielding will not be able to completely protect from this exposure (Chancellor et al. 2014). Unlike most SPE particles that can be stopped by adequate shielding, GCR particles penetrate the spacecraft and other surfaces, producing secondary particles including neutrons, lighter nuclei, electrons and gamma rays (Warden and Bayazitoglu 2019). Therefore, to encompass the various energies and dose rates that astronauts may be exposed to, the MIE was defined as “deposition of energy”, which is relevant to all types of radiation. With this MIE, the AOP network will have broad applicability to other domains outside of the space field (i.e., medical, occupational, environmental). Although the AOP begins with deposition of energy, other MIEs can be included in the future to expand the network, as AOPs are living documents that evolve as the science advances.

## 4.2 | AOs

Non-cancer outcomes were the initial focus of the AOP network because while cancer risks from various stressors, including radiation, are extensively studied and relatively well understood, non-cancer endpoints often remain less explored. The four AOs that were interconnected in the network included bone

loss (https://www.aopwiki.org/aops/482), cataracts (https://www.aopwiki.org/aops/478), learning and memory impairment (https://www.aopwiki.org/aops/483), and occurrence of abnormal vascular remodeling (https://www.aopwiki.org/aops/470). As described below, there is a sufficient body of human studies to support their regulatory significance.

Bone loss is a well-documented AO related to space flight due to changes in microgravity (Willey et al. 2011). However, growing evidence suggests that bone loss can also result from radiation exposure (Wright 2018; Willey et al. 2013). The available human data for bone loss come primarily from studies on returning astronauts (Stavnichuk et al. 2020). Examining astronauts from Apollo, International Space Station (ISS), and various other low Earth orbit missions revealed bone loss in the lower skeleton and preserved bone in the upper skeleton (Stavnichuk et al. 2020). Clinical studies on radiotherapy patients with pelvic, breast, and rectal cancers using high doses of fractionated ionizing radiation showed an increased risk of fractures to hips, ribs, femoral head, and neck arising 5-10 years post-radiotherapy (Baxter et al. 2005; Willey et al. 2011; Williams and Davies 2006; Oeffinger et al. 2006). It is established that chronic low-dose radiation contributes to bone loss, but there is less research on the effects of long-duration ionizing radiation exposure (Stavnichuk et al. 2020).

Radiation exposures are also associated with abnormal vascular remodeling and an increased risk of CVD (Boerma et al. 2016; Little et al. 2023). High-dose clinical trials, including studies with radiotherapy patients (Zou et al. 2019), indicate an increased CVD risk. Low-dose retrospective and prospective cohort studies utilize Japanese atomic bomb survivors, protracted occupational exposures, and Chornobyl survivors. Findings
from such epidemiological studies include an increased risk
of CVD morbidity (Zielinski et al. 2009) and mortality (Gillies
et al. 2017; Ivanov et al. 2006; Kashcheev et al. 2017) but study
designs have historically limited the strength of these studies
(e.g., small sample size). Atomic bomb survivor studies have
identified the dose influencing CVD outcome risk (Ozasa
et al. 2012; Preston et al. 2003; Shimizu et al. 2010; Takahashi
et al. 2017). A recent meta-analysis of 93 epidemiological studies
highlights significant uncertainties regarding confounding fac-
tors that may affect the association between radiation and CVD
(Little et al. 2023). Although excess radiation risk has not been
reported in astronauts, it is a priority for investigation (Patel
et al. 2020).

Evidence suggests that radiation exposure can lead to reduced
cognitive function (Collett et al. 2020). However, the exact mech-
anisms underlying this remain poorly understood (Pasqual
et al. 2021). Cognitive impairment related to radiation exposure
has been observed in various populations, including radiother-
apy patients (Krull et al. 2018), atomic bomb survivors (Yamada
et al. 2016), and occupationally exposed individuals such as
nuclear workers, uranium miners, medical workers, Mayak
workers, and Chornobyl clean-up workers (Azizova et al. 2020;
Lestaevel et al. 2016; Taormina et al. 2008). Additionally, animal
studies have contributed to this understanding (Cekanaviciute
et al. 2018) but the direct mechanisms have yet to be fully eluci-
dated. The determination of cognitive effects is complicated by
various assessment methods used and uncertainty around the
effects of low to moderate doses (Pasqual et al. 2021).

Ionizing radiation is also a well-established cataractogen (Ainsbury et al. 2016), supported by abundant evidence from animal and human epidemiological studies. Cohorts of atomic bomb survivors (Minamoto et al. 2004), Mayak workers (Azizova et al. 2016), Chornobyl clean-up workers (Worgul et al. 2007), astronauts (Chylack Jr et al. 2009, 2012), and aviators (Jones et al. 2006) were used to derive evidence for the International Commission on Radiological Protection (ICRP) to set eye lens dose limits (ICRP 2012; Rao 2016). Posterior subcapsular cataracts are the most common type of cataract that develops after exposure to ionizing radiation. While cataracts develop after either acute or chronic exposure to ionizing radiation (Hamada, Azizova, and Little 2020), there are relatively limited data from astronauts on the role of cosmic radiation in the increased risk of cataracts. Further research is needed at lower doses and dose rates to fully understand the mechanisms and develop effective countermeasures.

5 | Description of the KEs

5.1 | Macromolecular/Cellular Events

The initial screening of the literature identified biological events
leading to the four AOs of interest. Not all are included in the
final AOP network due to either the lack of a well-defined mea-
surement method or the lack of empirical evidence to support
connectivity to another KE. Existing KEs used in our proposed
pathways are linked to AOP #15, 72, 136, 139, 141, 216, 238,
272, 293, 294, 296, 303, 322, 397, 409, 420, 432, 443, and 451
in the AOP-Wiki. The KEs with strong biological plausibility

are described below, with further details found in the individ-
ual AOP reports (Kozbenko et al. 2024; Carrothers et al. 2024;
Sleiman et al. 2024; Sandhu et al. 2024). Empirical evidence
to support each KER is drawn predominantly from radiation
stressors (accounting for ~76% of the studies), altered gravity
(~17%), and multiple space stressors (~8%) (Figure 4). It is im-
portant to note that not all stressors reported support each KER;
for details on individual KERs, refer to the AOP-Wiki.

The AOP network begins with deposition of energy, which refers to events where energetic subatomic particles, nuclei, or electromagnetic radiation deposit energy in the media through which they transverse. The energy may either be sufficient (e.g., ionizing radiation) or insufficient (e.g., non-ionizing radiation) to ionize atoms or molecules. Energy deposition into matter may lead to a biological cascade of damage depending on the type (photons, electrons, protons, neutrons, etc.) and energy of the radiation. Densely ionizing radiation produces more irreparable clustered damage (Mavragani et al. 2019). Energy absorption may occur within 10⁻¹⁷ to 10⁻¹³ s following radiation exposure to living cells (Bouten et al. 2021). However, not all deposited energy initiates a path to disease/adversity as sufficient damage is required to overwhelm the natural homeostatic processes for defense.

Deposited energy may directly or indirectly damage macromol-
ecules within the cell. The indirect damage is initiated with
the breakage of water molecules to hydrogen and hydroxyl
ions. This hydrolysis generates free radicals or reactive oxygen
species (ROS) which may occur within 10⁻¹⁰ to 10⁻⁶ s (Bouten
et al. 2021). The further interaction of free radicals with ni-
tric oxide (NO) molecules within cells, produced from nitric
oxide synthase (NOS), reduces NO levels and leads to reactive
nitrogen species (RNS) generation. Together, the accumula-
tion of free radical damage through various sources (e.g., ra-
diolysis of water, mitochondrial damage) (Desouky et al. 2015;
Mittal et al. 2014; Soloviev and Kizub 2019) may overwhelm
antioxidant enzymes and free radical scavengers. Low antiox-
idant production/level then leads to a state of oxidative stress
(AOP #470) (Slezak et al. 2017; Tahimic and Globus 2017; Wang
et al. 2019).

Oxidative stress may damage macromolecules (DNA, lipids, proteins) leading to by-products (e.g., through peroxidation) that may activate resident cells, causing an increase in pro-inflammatory mediators (Chang et al. 2017; Mavragani et al. 2019; Hladik and Tapio 2016; Ping et al. 2020). In turn, cells may transmit damage signals through gap junction-mediated intercellular channels and by secreting cytokines and ROS into the extracellular matrix, leading to the further spread of oxidative stress, damage, and altered signaling in neighboring non-irradiated cells (Little et al. 2023; Hamada et al. 2007, 2011). In the minutes to hours following the initial damage, ROS may activate nuclear factor κB (NF-κB), leading to the production of various pro-inflammatory mediators, including tumor necrosis factor α (TNF-α), interleukin 1 (IL-1), and interleukin 6 (IL-6). Exposure to ionizing radiation may also release alarmins and damage associated molecular patterns (DAMPs) that promote activation of nearby resident immune cells (AOP #483) such as macrophages and dendritic cells in the brain. Immune cell activation through pattern recognition of the DAMPs may
<figure><img src="image_4.png" /><figcaption>**FIGURE 4** | Summary of the (A) Evidence streams, and (B) Stressor types used to support the AOP.</figcaption></figure>

promote inflammation (AOP #483), cause regeneration of the affected cells and tissues, and promote proliferation and recruitment of additional immune cells (Chen et al. 2016; Collett et al. 2020; Cucinotta et al. 2014; Hladik and Tapio 2016; Schaue et al. 2015). This activation may alter the activity of receptors and disrupt normal cellular signaling processes. Altered signaling is a shared KE across several AOPs and is a central connector in the network.

Cell signaling, particularly the stress response, may vary depending on cell type and anatomical location. In response to DNA damage, many signaling pathways are initiated. Resultant changes in signaling pathways explored in our AOP network include: the phosphatidyl inositol 3-kinase (PI3K)/ Protein kinase B (Akt) pathway, cAMP response element-binding protein (CREB) pathway, the p53/p21 pathway, as well as the mitogen-activated protein kinase (MAPK) family pathways, including c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK) and p38 (Lehtinen and Bonni 2006; Ramalingam and Kim 2012). Together, these alterations may result in changes in cell cycle, senescence, apoptosis, and proliferation (Betlazar et al. 2016). For instance, in many cell types, phosphorylation of p53 may occur, stabilizing the protein and propagating cellular senescence or triggering a caspase cascade that results in apoptosis (Hein et al. 2014). In endothelial cells, the PI3K/Akt/mammalian target of rapamycin (mTOR) signaling pathway may modulate the levels of endothelial nitric oxide synthase in response to different radiation exposure scenarios (Yentrapalli et al. 2013; Azimzadeh et al. 2015, 2017, 2021; Vieira Dias et al. 2018; Hamada, Kawano, et al. 2020). Similarly, in bone cells, the activity of osteoblasts and osteoclasts, governed by signaling pathways related to cell differentiation, dictates bone generation and

resorption. Specifically, the binding of receptor activator of NF-κB (RANK) to its ligand (RANK-L) triggers the differentiation of hematopoietic stem cells into pre-osteoclasts and osteoclasts (Donaubauer et al. 2020; Smith 2020). Conversely, osteoprotegerin (OPG) may competitively bind to RANK-L. In turn, RANK-L inhibits osteoclast differentiation and reduces bone resorption, leading to bone loss. Thus, the balance between cell death and proliferation across various tissues may be significantly influenced by direct damage to proteins, DNA, or cells and is regulated by many concerted signaling pathways.

Alongside the indirect damage caused by energy deposition, direct damage may also occur. Deposition of energy leading to ionization events may also concurrently cause direct bond breakage of proteins and DNA molecules, leading to various molecular modifications (Fochler and Durchschlag 1997). A multitude of reviews suggests that at sufficient levels of ionizing radiation, increased protein modifications (AOP #478) may occur in the form of deamidation, oxidation, disulfide bonds, cross-linking, and protein aggregation (AOP #478) (Fochler and Durchschlag 1997). Additionally, there may be transcriptional, translational, or post-translational modifications. Increases or decreases in protein expression, along with aggregation, may result in a tissue or organ level changes and further alterations in signaling.

In addition to protein modifications, DNA strand breaks result from ionization events. Ionizing radiation interacts directly with DNA, causing single-strand breaks (SSBs), double-strand breaks (DSBs), or clustered lesions (Rydberg 1996; Prise et al. 1999; Sutherland et al. 2000; Fakir et al. 2006; Timm et al. 2018). Indirectly, ROS generation may break DNA strands (AOP #478)
(Mavragani et al. 2015; Nikjoo et al. 2016). These lesions may involve several types of damage to the DNA within a small area, typically 10 bp (Nikjoo et al. 2016). These lesions are caused by oxidation of the nitrogenous bases, with a particular propensity for guanine because of its low oxidation potential (Jovanovic and Simic 1986). The oxidation and damage to DNA may be detected within seconds to hours after radiation exposure (Bouten et al. 2021). The error-prone repair mechanism of non-homologous end joining (NHEJ) of clustered damage tends to produce non-reversible DNA damage (Mavragani et al. 2015). Under normal conditions, the DNA may be repaired properly; however, during persistent oxidative stress, accumulation of large numbers of clustered lesions overloads the repair mechanisms, resulting in mutations such as insertions, deletions, and mismatches from incorrect repair (Turner 2002; Schoenfeld et al. 2012; Tangvarasittichai and Tangvarasittichai 2018). The damage may in parallel cause structural changes to the DNA backbone, alter its transcriptional access or replication, and create instability of the genome through chromosomal aberrations (Markkanen 2017). These molecular disruptions, if unresolved, cascade into broader tissue-level consequences, where the accumulation of cellular damage and loss of cellular integrity may lead to alterations in tissue structure and function, ultimately affecting organ homeostasis as described below.

## 5.2 | Tissue/Organ/Organism Level Events

Within the days to weeks following deposition of energy, the accumulated cellular damage that is not repaired contributes to a modification of tissue structure and function. Chronic inflammation over months to years may lead to sustained damage and a dysregulated cellular environment that contributes to age-related phenotypes. Depending on the site of damage and the cell type, different adversities may ensue. Such pathologies may lead to many diseases, and the four target AOs examined in the AOP network include tissue/organ level effects on the bone, brain, eye, and vasculature. As described above, these pathways share common macromolecular and cellular-level KES, such as oxidative stress, DNA damage, and altered signaling. However, their mechanisms diverge into tissue and organ specific events, as outlined below.

In the context of the bone loss AOP, unrepaired damage to DNA, proteins, and lipids induced by free radicals may trigger cell death. Chronic oxidative stress and inflammation further disrupt cellular homeostasis, leading to the activation of apoptotic pathways, often via mitochondrial dysfunction. Both apoptosis (programmed cell death) and necrosis may occur within hours to days following exposure, depending on the extent of damage. For instance, ROS-induced DNA damage may activate p53, which in turn promotes apoptosis (Ott et al. 2007). Oxidative stress is also associated with cell death through mitochondrial dysfunction, wherein calcium channels are opened, leading to the release of apoptotic factors and cellular swelling (Jilka et al. 2013; Memme et al. 2021; Sasi et al. 2015). Additionally, ROS may enhance autophagy by activating the transcription factor EB (TFEB)-mediated signaling cascade (Johnson et al. 2020), thereby altering cellular signaling. TFEB regulates the autophagy-lysosomal pathway, promoting the generation of autophagosomes under stressful conditions, such as when calcium is released from the

lysosome (Song et al. 2021; Zhang et al. 2016). While autophagy is a normal physiological process in osteoblasts (Chatziravdeli et al. 2019; Guo et al. 2021), excessive autophagic flux may lead to cell death (Li et al. 2020). When combined with osteoclastic resorption and osteoblastic ossification, cell death exacerbates bone cell dysfunction. Disrupted signaling in osteoblasts and osteoclasts leads to dysregulated bone cell homeostasis, favoring bone resorption through an increased RANK-L/OPG ratio, which stimulates osteoclastogenesis (Chatziravdeli et al. 2019; Jilka et al. 2013; Komori 2013). Furthermore, osteocyte apoptosis results in the release of pro-osteoclastogenic factors, further promoting osteoclastogenesis and enhancing bone resorption (Komori 2013). At the same time, osteoblasts may undergo apoptosis or autophagy in response to ionizing damage, which reduces their activity and impairs bone formation (Wang et al. 2020; Xiong and O'Brien 2012). These alterations in osteoblast and osteoclast function disrupt bone homeostasis and remodeling, ultimately leading to a loss of bone matrix into the bloodstream and/or a reduction in bone deposition, culminating in bone loss (AO) (Donaubauer et al. 2020; Smith 2020; Willey et al. 2011).

For learning and memory impairment, ROS and increased DNA strand breaks may alter signaling pathways like MAPK or promote neuroinflammatory environments, causing neural remodeling. The signaling cascades lead to the expression of pro-inflammatory cytokines, triggering an inflammatory response within hours. Adhesion molecules like intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1) may promote the entry of immune cells into tissues, where a chronic state of inflammation may induce senescence and cell death and impact neuronal morphology and neurogenesis in the brain (Tang et al. 2017; Wang et al. 2019). This encompasses various neuronal processes such as neurogenesis, neurodegeneration, and neuronal excitability, which impair learning and memory (AO) (Bálentová and Adamkov 2020; Monje and Palmer 2003). In addition, structural alterations such as demyelination, synaptic plasticity, and dendritic spine density may impair learning and memory (Donzis and Tronson 2014; Rachal Pugh et al. 2001; Yirmiya and Goshen 2011).

For the cataract AOP, deposition of ionizing radiation energy may cause DNA strand breaks, chromosomal aberrations, and changes to DNA structure, and an overall increase in mutation rate due to the cellular state of oxidative stress and poor repair mechanisms for DSBs. Such effects may alter oncogenes, which tend to promote proliferation when turned on/off by a mutation (Pitolli et al. 2019). For example, p53 is a tumor suppressor protein that arrests cells in the G1 phase (Chen et al. 2016). However, ionizing radiation at a high enough dose may cause irreparable DNA damage that inactivates p53, thereby causing uncontrolled cell proliferation (AOP #478) (Khan and Wang 2022). If this occurs in the lens of the eye, where there is little turnover, over time opacification will occur because of the rapid proliferation and incomplete differentiation in the germinative zone (GZ) of the lens epithelium (Ainsbury et al. 2016). In the GZ of the lens, rapid proliferation of lens epithelial cells may lead to incomplete differentiation of lens fiber cells. As these cells progress through the lens, they may retain organelles that would normally be lost during proper maturation. This failure to fully differentiate results in reduced lens transparency and
contributes to the development of cataracts (AO) (Ainsbury et al. 2016; Hamada 2017; McCarron et al. 2022).

Oxidative stress in the vascular endothelium alters NO levels (AOP #470) in vascular endothelial cells. NO maintains vascular homeostasis by regulating vascular dilation, local cell growth, and platelet formation. ROS is able to uncouple eNOS (the enzyme that is used to synthesize NO from L-arginine in endothelial cells) causing the production of more ROS, which convert NO to peroxynitrite, both of which reduce levels of available NO, causing endothelial dysfunction (AOP #470) (Förstermann 2010; Förstermann and Münzel 2006; Mitchell et al. 2019; Nagane et al. 2021; Soloviev and Kizub 2019). Indeed, lower cellular and serum levels of NO have been observed after different radiation exposures in many studies (Azimzadeh et al. 2015, 2017, 2021; Ait-Aissa et al. 2022). Vascular endothelial cell dysfunction is associated with a lack of NO from oxidative stress, altered cell signaling, and prolonged inflammation from inflammatory mediators (Baran et al. 2021; Bonetti et al. 2003; Deanfield et al. 2007; Krüger-Genge et al. 2019). This pathological activation of the endothelium generates senescent endothelial cells, which further contribute to disruption in vascular homeostasis along with phosphorylation of p53, leading to endothelial cell apoptosis (Borghini et al. 2013; Hughson et al. 2018; Schiffrin 2008; Senoner and Dichtl 2019; Soloviev and Kizub 2019; Venkatesulu et al. 2018). Abnormal vascular remodeling (AO) occurs in an effort to compensate for dysfunction. Some methods to combat non-laminar flow and impaired repair include increasing vascular stiffness through thickening (Hsu et al. 2019), promoting leukocyte adhesion and fibrosis to aid repair (Sylvester et al. 2018), and enhancing arterial stiffness by increasing collagen and smooth muscle content (Zieman et al. 2005). Additionally, during periods of low NO,

there is a reduction in elastin and extracellular matrix components (Hsu et al. 2019; Sylvester et al. 2018). These changes may culminate in accelerated atherosclerosis and a higher likelihood of thrombosis and embolisms (Boerma et al. 2016; Hughson et al. 2018; Slezak et al. 2017; Sylvester et al. 2018; Vernice et al. 2020).

Together, these KEs form the well-established understanding of the mechanisms underlying this AOP network. The timeframe for these processes spans from immediate molecular damage to chronic tissue dysfunction and disease progression over months to years, illustrating the complex and prolonged impact of deposition of energy on biological systems.

## 6 | Domain of Applicability

The AOP network is applicable to vertebrates across all life stages and both sexes (Figure 5). The majority of the taxonomic evidence to support the causality of the network interactions is derived from studies in humans (Homo sapiens), human-derived cell lines, mouse (Mus musculus), and rat (Rattus norvegicus) models, with less evidence coming from rhesus monkey (Macaca mulatta), rabbit (Oryctolagus cuniculus), and beagle dog (Canis lupus familiaris) models. The AOP is applicable to both sexes; however, most of the data to support the AOs were collected from male animals. This AOP network is relevant to all life stages; however, most available data (with specified life-stage) across species are derived from adult models. It should also be noted that bone loss, abnormal vascular remodeling, cataracts, and learning and memory impairment can be more prevalent in the middle-aged population, and for most AOs, elderly populations were used in the evaluation of empirical studies.

<figure><img src="image_5.png" /><figcaption>**FIGURE 5** | Summary of the domain of applicability. *Studies derived from in vitro models do not specify life stage.</figcaption></figure>
## 7 | Summary of Empirical Evidence

The modified Bradford-Hill criteria of biological plausibility, essentiality of the KE, dose/incidence/temporal concordance, and quantitative understanding were used to evaluate the weight of evidence supporting the KERs and the AOPs.

Across the AOP network, the biological plausibility was considered strong for most KERs. The biological plausibility of the network was supported by extensive evidence demonstrating that most KERs are structurally and functionally well understood. The essentiality of most KEs was considered moderate, as a few knockout and transgenic animal models, as well as chemical modulators, provided insights into the roles of specific KEs at each step of the pathway. For example, studies elucidated how ROS-induced DNA damage can lead to the formation of mutagenic lesions, which disrupt transcriptional and replicative processes, ultimately driving the pathway toward the AO (Liu et al. 2013; Turner et al. 2002). These structural insights are reinforced by functional studies demonstrating that the loss of key proteins, like RANK-L in osteoblasts, disrupts bone remodeling and leads to exaggerated osteoclastogenesis, thereby impairing bone homeostasis (Chandra et al. 2014). Additionally, chemical modulation of autophagy pathways has revealed how dysregulated autophagic flux can result in cell death and tissue dysfunction, further supporting the functional relevance of these KEs in pathological states (Li et al. 2020; Wang et al. 2020). These findings provide moderate evidence that the individual KEs are not only mechanistically interlinked but also essential for the progression of AO. Collectively, the integration of structural and functional evidence strengthens the causal relationships within the pathway.

Most empirical evidence was derived from observational and experimental studies conducted using animal or in vitro models. While data from human space environment studies are limited, terrestrial radiation studies provided a substantial body of evidence. However, there was a lack of studies that utilized a broad range of doses and multiple time points, which are essential for effectively assessing the dose- and time-dependence of KEs across the AOPs. The frequency of KEs was not often measured. KERs linked to deposition of energy tended to have stronger dose, time, and incidence support, due to more historical data originating from various radiation stressors. KERs from oxidative stress to the various KEs related to abnormal vascular remodeling and cataract AOs had less empirical evidence. Overall, the KER deposition of energy to oxidative stress contained a large body of consistent evidence to support dose- and time-concordance across these KEs. Weakly supported KERs were predominantly adjacent KERs (e.g., altered signaling to increase endothelial dysfunction).

Due to the lack of strong empirical evidence for adjacent KERs, quantitative understanding of the AOP network is generally low. This is partly due to the lack of standardized reporting for data and assays, which complicates the ability to draw clear connections between upstream KEs and downstream effects. Moreover, many of the KERs are multifaceted, with numerous parameters and modulating factors (see below) contributing to the complex relationships. Currently, mathematical models do not exist that can reliably predict downstream effects based on upstream events; this could be an area of future research focus.

Some conflicting results have been observed; largely due to variability in study design, including differences in dose, dose rates,

<table><caption>Vascular Remodeling</caption><thead><tr><th></th><th colspan="9">KER #</th></tr><tr><th></th><th>2811</th><th>3315</th><th>3316</th><th>2772</th><th>3317</th><th>2774</th><th>3318</th><th>2777</th><th>2784</th><th>2789</th></tr></thead><tbody><tr><th>Biological Plausability</th><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td></tr><tr><th>Empirical Evidence</th><td>High</td><td>High</td><td>High</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td></tr><tr><th>Quantitative Understanding</th><td>Moderate</td><td>Moderate</td><td>Low</td><td>Moderate</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><th>Essentiality</th><td>High</td><td>Low</td><td>High</td><td>High</td><td>Moderate</td><td>High</td><td>Moderate</td><td>Low</td><td>Moderate</td><td>Moderate</td></tr></tbody></table>

<table><caption>Cataracts</caption><thead><tr><th></th><th colspan="9">KER #</th></tr><tr><th></th><th>2810</th><th>2811</th><th>2812</th><th>1909</th><th>1911</th><th>164</th><th>1912</th><th>2816</th><th>2819</th><th>1913</th></tr></thead><tbody><tr><th>Biological Plausability</th><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>Moderate</td><td>Moderate</td><td>Moderate</td></tr><tr><th>Empirical Evidence</th><td>Low</td><td>Moderate</td><td>Low</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Low</td><td>Low</td><td>Moderate</td></tr><tr><th>Quantitative Understanding</th><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Moderate</td><td>Moderate</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><th>Essentiality</th><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Low</td><td>High</td><td>High</td><td>Low</td><td>Moderate</td><td>Moderate</td></tr></tbody></table>

<table><caption>Bone Loss</caption><thead><tr><th></th><th colspan="6">KER #</th></tr><tr><th></th><th>2716</th><th>3319</th><th>2842</th><th>3320</th><th>2844</th><th>2845</th></tr></thead><tbody><tr><th>Biological Plausability</th><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td><td>High</td></tr><tr><th>Empirical Evidence</th><td>Moderate</td><td>High</td><td>High</td><td>High</td><td>Moderate</td><td>Moderate</td></tr><tr><th>Quantitative Understanding</th><td>Low</td><td>Low</td><td>Low</td><td>Moderate</td><td>Low</td><td>Low</td></tr><tr><th>Essentiality</th><td>High</td><td>High</td><td>High</td><td>High</td><td>Low</td><td>Moderate</td></tr></tbody></table>

<table><caption>Learning & memory Impairment</caption><thead><tr><th></th><th colspan="9">KER #</th></tr><tr><th></th><th>3316</th><th>2833</th><th>2834</th><th>2835</th><th>2836</th><th>3321</th><th>2841</th><th>2811</th><th>3315</th></tr></thead><tbody><tr><th>Biological Plausability</th><td>High</td><td>Moderate</td><td>High</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>High</td><td>High</td></tr><tr><th>Empirical Evidence</th><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td></tr><tr><th>Quantitative Understanding</th><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td><td>Low</td></tr><tr><th>Essentiality</th><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td><td>Moderate</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**FIGURE 6** | A summary of the evidence evaluation of adjacent key event relationships in the four adverse outcome pathways of cataracts, abnormal vascular remodeling, learning and memory impairment, and bone loss.</figcaption></figure>
time points, radiation quality, experimental models, and clinical endpoints (e.g., incipient vs. mature cataracts). These factors should be considered in future studies to enhance the consistency and robustness of the empirical evidence, as well as to improve the quantitative understanding of the AOP network. A summary of the empirical evidence can be found in Figure 6.

## 8 | Modulators

The AOP network can be modulated by a variety of factors such as age, sex, drugs, and genetics among others. Hormonal changes associated with aging can influence the risk of developing various diseases. Estrogen levels decrease with age, which can lead to decreased bone formation and increased bone resorption (Pacheco and Stock 2013; Willey et al. 2011). Furthermore, hormonal changes can cause the loss of elasticity in blood vessels and a build-up of plaque that is associated with structural and functional changes to the endothelium. Accumulated damage from decreased DNA repair capacity with age increases the risk of CVD (Olivieri et al. 2013; North and Sinclair 2012; Vitale et al. 2009). Although men have a higher incidence of CVD than women, the risk is increased in post-menopausal women (Garcia et al. 2016; Vitale et al. 2009). Additionally, some studies have shown that radiation-induced cardiovascular events and mortality are significantly higher in women than men following radiotherapy (Khalid et al. 2020). Other studies suggest that females may be more susceptible to radiation-induced cataracts than men (Azizova et al. 2018); however, more research is needed to fully understand this relationship (Barnard and Hamada 2023). In terms of brain disorders, a study in a mouse model of simulated GCR exposure showed that male mice, but not female mice, developed cognitive deficits, microglia activation, and synapse loss (Krukowski et al. 2018). Again, more research is needed to explore these sex differences, particularly due to the historical bias toward the more frequent use of male rodents than female ones, for various reasons (Mauvais-Jarvis et al. 2017), which has led to knowledge gaps surrounding the female system.

Certain drugs modulate KERs in the AOP to bone loss, abnormal vascular remodeling, cataracts, and learning and memory impairment. For example, osteoclast inhibitors and osteoblast activators have a preventative effect against bone loss (Wissing 2015; Chandra et al. 2014; Yoshida et al. 2002), while some xenobiotics accelerate ionizing radiation-induced bone loss (Rana et al. 2012). Anti-inflammatory medications such as aspirin are potentially preventive against the development of radiation-associated CVD (Camara Planek et al. 2020). Additionally, anti-inflammatory agents reduce activated microglia and prevent learning and memory impairment (Monje and Palmer 2003; Greene-Schloesser and Robbins 2012). Antioxidants also reduce levels of oxidative stress and subsequent disease progression (Domazetovic et al. 2017; Ostman et al. 2009; Manda et al. 2007; Thiagarajan and Manikandan 2013). The most well-studied modulators of oxidative stress include: sulforaphane, epigallocatechin-3-gallate (EGCG), gallocatechin gallate (EGCG epimer), edaravone, fucoxanthin, nitroxides, thiols, and diphenyliodonium.

Another modulator of the AOP is genetics (Hamada and Fujimichi 2015; Barnard and Hamada 2023). Mutations in genes regulating bone metabolism, calcium absorption, and

vitamin D metabolism is implicated in modulating bone loss (Mitchell and Yerges-Armstrong 2011; Ranganathan 2009; Bai et al. 2020). There are several genomic loci associated with heart disease (Nelson et al. 2017; Nikpay et al. 2015). Sex-specific genetic risk factors related to vascular remodeling can also contribute to CVD modulation (Berk and Korshunov 2006). Additionally, genes associated with lipid metabolism such as apolipoprotein E4 (ApoE4) variants are associated with an increased risk of CVDs (Mahley 2016). ApoE4 is also associated with an increased risk of developing Alzheimer's disease (Hunsberger et al. 2019; Zhu et al. 2012). Certain genetic polymorphisms related to DNA repair processes and antioxidant defense mechanisms may increase the risk of radiation-induced cognitive impairment (Bucholtz and Demuth 2013).

Lifestyle factors, particularly diet, smoking, physical activity, and pre-existing diseases can strongly influence the risk of a number of diseases (Mozaffarian et al. 2008; Turner et al. 2002; Little et al. 2024; Richardson 2022; Richardson et al. 2020). For example, physical activity and nutrition have been shown to prevent bone loss (Sibonga et al. 2019; Ackerman et al. 2021). Other examples are detailed in the AOP-Wiki.

Overall, this proposed AOP network can be influenced by these modulators, which can alter the biological response to stressors. Understanding how these factors modify KEs within an AOP is an area for further exploration and is necessary for developing quantitative relationships.

## 9 | Challenges and Knowledge Gaps

The development of this AOP network uncovered several uncertainties and knowledge gaps, with the major ones described here.

### 9.1 | Inconsistent Findings on Measurement Levels

Some of the observed inconsistencies stem from the lack of clear, objective definitions and standardized measurements for certain KEs. For instance, the absence of a well-defined endpoint for cataracts vs. lens opacities complicates literature searches and the establishment of accurate KERs. Similarly, defining broad endpoints such as CVD is challenging due to its complex etiology, which led to the adoption of a more specific endpoint: abnormal vascular remodeling. The KE "abnormal neuronal remodeling" also presented challenges during AOP development, as it encompasses a wide range of endpoints. In the case of NO levels, inconsistencies may arise from the diverse proxy measures used for its detection, as direct measurement of NO is difficult to conduct (Luiking et al. 2010). Further standardization in NO measurement could help refine the AOP to specify that this KE is linked to NO depletion.

### 9.2 | Lack of Female-Specific Mechanistic Data

Many KERs lack female-specific mechanistic data. Typically, male models were studied, with females being less represented
throughout the entire network. Human spaceflight studies examining sex differences tend to have low sample sizes because women make up less than 20% of astronauts (Lang et al. 2017). Some animal model studies that did examine both sexes did not separate male from female results (Chen et al. 2005; Lee et al. 2020; Russell et al. 2009).

## 9.3 | Age-Related Effects

Few mechanistic studies investigate the impact of age-related effects on the four non-cancer outcomes after radiation exposure.

## 9.4 | Chronic and Low-Dose Exposure Data

There is a relative lack of chronic and low-dose (<0.5 Gy (EPRI 2021)) exposure data, and several studies found unexpected or inconsistent effects of low dose radiation. There are currently efforts to create an experimental radiation field exposure scenario with more similarity to space (Norbury et al. 2016; Huff et al. 2023), which will be important in strengthening this AOP network.

## 9.5 | Dose-Concordance Discrepancies

Inconsistencies in dose-concordance data between many KERs such as oxidative stress and pro-inflammatory mediators complicate the understanding of their interplay.

## 9.6 | Multiple Stressors

There are insufficient studies examining the combined effects of multiple space stressors on the bone, heart, eye, and brain. There is a need for more data relevant to space radiation (e.g., heavy ions, alpha particles, neutrons).

## 9.7 | Qualitative Nature of the AOP

The proposed AOP network is qualitative and includes various radiation types and doses, necessitating an eventual quantitative understanding of dose, dose rate, and radiation quality effects on KEs and KERs.

## 9.8 | Standardization of Measurement

There is a need for standardized methods to measure and interpret endpoints to refine the AOP and specify the KE.

## 9.9 | Consistent Data Reporting

Prioritization is required for experiments that use the same experimental platform to assess multiple KEs in an AOP. This will allow comparison of results and facilitate the understanding of time concordance and dose concordance effects. Consistent data reporting is needed to validate quantitative aspects of the relationships between KERs.

## 9.10 | Relevant Models

KERs lacked evidence from models that were relevant to the respective AO, which gave poor inferential strength to the KER. For example, there were no lens-specific data for some KERs (e.g., inadequate DNA repair to increase mutations; oxidative DNA damage to inadequate DNA repair; increase DNA strand breaks to inadequate DNA repair; inadequate repair to chromosomal aberrations; increase mutations to cell proliferation; chromosomal aberration to increase cell proliferation) within the cataracts pathway, making it unclear how these KEs contribute specifically to cataracts. Alternatively, some KERs (e.g., oxidative stress to DNA strand breaks; pro-inflammatory mediators to abnormal neural remodeling) within the impaired learning and memory pathway had minimal evidence from human studies to evaluate the modified Bradford-Hill criteria.

## 9.11 | Adaptive Responses

Adaptive responses have also been observed in animal and in vitro models, where low doses of radiation can induce a protective effect (reviewed in Guéguen et al. 2019); but its implications for human health effects are not clear.

## 10 | Actionable Recommendations

To fully leverage the AOP framework for risk assessment purposes, we recommend key steps that must be taken to improve the quantitative understanding of the AOP network through new experimental research (Table 2):

* *Select and measure more than one KER in a study:* Design and conduct experiments that identify and quantify at least two KERs at different stages of the AOP (e.g., molecular, cellular, tissue-level, organism-level). These should capture early damage responses (e.g., DNA damage, oxidative stress) and link to downstream phenotypic effects (e.g., inflammation, functional/behavioral impairment). Including multiple KEs in experiments is essential to understanding both the qualitative and quantitative relationships across the AOPs.

### 10.1 | Use Both In Vitro and In Vivo Models

* Employ relevant 2D/3D in vitro models (e.g., organoids, organ-on-chip) that reflect tissue-specific responses and allow for high-throughput mechanistic investigations. In parallel, utilize appropriate animal models to evaluate systemic and higher-order effects that cannot be captured in vitro, such as learning and memory. Selection of the species should be guided by the specific AOs under investigation and the suitability of the model for measuring relevant AO-specific phenotypic endpoints. *Use a Broad Dose Range and Multiple Timepoints:* Design experiments to cover a wide range of doses, including both low-dose and high-dose exposures relevant to space radiation. Conduct assessments at multiple time points to capture both early and late events along the AOP, enhancing temporal mapping of the AOP.
<table><caption>TABLE 2 | Recommended models and endpoints across biological levels for selected adverse outcome pathways (AOPs).</caption><thead><tr><th>Adverse outcome (AO)</th><th>Key event relationship (KER)</th><th>Relevant cell types</th><th>Best models</th><th>Molecular-level endpoints</th><th>Cellular-level endpoints</th><th>Organism-level endpoints</th></tr></thead><tbody><tr><td>Bone loss</td><td>Oxidative stress to Oxidative DNA damage to Bone cell homeostasis to bone remodeling (Osteoblast/osteoclast disruption leading to bone resorption)</td><td><ul><li>Osteoblasts</li><li>Osteoclasts</li><li>Bone marrow stromal cells (BMSCs)</li></ul></td><td><ul><li>3D organoid model of bone tissue</li><li>Scaffold-based co-culture of osteoblasts and osteoclasts</li></ul></td><td><ul><li>DNA double-strand breaks (γ-H2AX)</li><li>Mitochondrial dysfunction (ATP levels)</li><li>Oxidative damage markers (8-OHdG)</li></ul></td><td><ul><li>Osteoclast differentiation markers (RANKL, OPG)</li><li>Calcium resorption</li><li>Inflammatory cytokines (IL-6, TNF)</li></ul></td><td>Mouse or Rat: Bone mineral density (DXA), trabecular microarchitecture (micro-CT), mechanical strength (three-point bending)</td></tr><tr><td>Abnormal vascular remodeling</td><td>Endothelial dysfunction to abnormal vascular remodeling (Endothelial cell injury leading to increased arterial stiffness or plaque formation)</td><td><ul><li>Endothelial cells</li><li>Smooth muscle cells</li><li>Pericytes</li></ul></td><td><ul><li>3D endothelial/vascular tissue-on-chip</li><li>Perfusable vascular networks with endothelial and smooth muscle cells</li></ul></td><td><ul><li>Endothelial DNA damage (γ-H2AX)</li><li>Lipid peroxidation (MDA)</li><li>Nitric oxide (NO) signaling disruption</li></ul></td><td><ul><li>Oxidative stress markers (ROS)</li><li>Endothelial dysfunction (VEGF, eNOS)</li><li>Smooth muscle proliferation</li><li>Vascular stiffness (elastin/collagen ratio)</li></ul></td><td>ApoE-/- or LDLR-/- Mice: Aortic plaque size (histology), arterial stiffness (pulse wave velocity)</td></tr><tr><td>Learning and memory impairment</td><td>Oxidative stress to proinflammatory mediators to neural remodeling (Neuronal oxidative stress leading to neuroinflammation and synaptic dysfunction)</td><td><ul><li>Neurons</li><li>Astrocytes</li><li>Microglia</li><li>Pericyte</li><li>Blood-brain barrier endothelial cells</li></ul></td><td><ul><li>3D cerebral organoids</li><li>Neuron-astrocyte co-culture models</li><li>Blood-brain barrier-on-chip</li></ul></td><td><ul><li>Lipid peroxidation (MDA)</li><li>Oxidative DNA damage (8-OHdG)</li><li>Protein oxidation (carbonyl content)</li></ul></td><td><ul><li>Neuroinflammatory markers (IL-1β, TNF-α)</li><li>Synaptic proteins (PSD-95)</li><li>Neuronal long-term potentiation</li></ul></td><td>Mouse or Rat: Morris water maze (spatial memory), novel object recognition (learning), fear conditioning (emotional memory, discrimination learning)</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 2 | (Continued)</caption><thead><tr><th>Adverse outcome (AO)</th><th>Key event relationship (KER)</th><th>Relevant cell types</th><th>Best models</th><th>Molecular-level endpoints</th><th>Cellular-level endpoints</th><th>Organism-level endpoints</th></tr></thead><tbody><tr><td>Cataracts</td><td>Oxidative stress to protein modification (Lens epithelial macromolecular damage leading to opacity)</td><td><ul><li>Lens epithelial cells</li><li>Lens fibers</li></ul></td><td><ul><li>Lens-on-chip models</li><li>3D lens organoids</li></ul></td><td>Oxidative stress markers (SOD, GPx)<ul><li>DNA damage markers (γ-H2AX)</li><li>Lipid peroxidation (4-HNE, MDA)</li></ul></td><td><ul><li>Protein cross-linking (lens crystallins)</li><li>Protein aggregation</li><li>Apoptosis markers (Caspase-3, BAX)</li><li>Lens transparency (functional marker of cataract formation)</li></ul></td><td>Mouse/Rat/Rabbit: Slit lamp exam for lens opacity, Scheimpflug imaging, histopathology</td></tr></tbody></table>

Note: This table summarizes key experimental models and endpoints considered relevant for AOP development. It is not exhaustive and should be adapted to specific research contexts. Abbreviations: 4-HNE, 4-hydroxy-2-nonenal; 8-OHdG, 8-hydroxy-2'-deoxyguanosine; ATP, adenosine triphosphate; BAX, Bcl-2 associated X-protein; BMSC, bone marrow stromal cell; eNOS, endothelial nitric oxide synthase; GPx, glutathione peroxidase; IL-1β, interleukin-1β; IL-6, Interleukin 6; KER, key event relationship; MDA, malondialdehyde; NO, nitric oxide; OPG, osteoprotegerin; PSD-95, postsynaptic density protein 95; RANKL, receptor activator of nuclear factor-kappa B ligand; ROS, reactive oxygen species; SOD, superoxide dismutase; TNF, tumor necrosis factor; TNF-α, tumor necrosis factor α; VEGF, vascular endothelial growth factor; γ-H2AX, the phosphorylated form (γ of the histone H2AX.

* **Assess Multiple Endpoints:** Measure macromolecular (e.g., DNA damage, oxidative stress) and cellular/functional endpoints (e.g., inflammation, cell viability, tissue remodeling, behavioral outcomes) aligned with KEs of the AOP to provide comprehensive data on the progression of radiation effects.

* **Incorporate Multi-scale and Multi-omic Technologies:** Use high-throughput omics technologies (e.g., genomics, proteomics, metabolomics) to integrate molecular data with phenotypic outcomes. This will provide predictive mechanistic understanding of the dose-response relationships.

* **Apply Dose-Response Modeling Approaches:** Use quantitative models to analyze the data, including benchmark dose modeling and point of departure identification (Chauhan et al. 2023; Vuong et al. 2025). This step will help to define the dose at which molecular-level damage progresses into observable AOs.

## 11 | Recommended Cell Types to Understand the Cellular/Molecular Mechanisms Leading to Each AO

* **Bone Loss:** Osteoblasts (bone formation), osteoclasts (bone resorption), and bone marrow stromal cells (BMSCs) for their role in bone remodeling.

* **Vascular Remodeling:** Endothelial cells, smooth muscle cells, and fibroblasts as they form the vascular structure and are affected by radiation-induced remodeling.

* **Learning and Memory Impairment:** Neurons, astrocytes (supporting cells), microglia (immune cells of the brain), brain pericytes, and blood-brain barrier endothelial cells, which are all involved in neuroinflammation and cognitive decline.

* **Cataracts:** Lens epithelial cells and lens fibers are central to cataract formation.

### 11.1 | Radiation Type

Space-relevant radiation types like heavy ions, neutrons, protons, and gamma radiation simulate exposure to GCR and SPEs. Consider well-characterized low and high doses, variable dose rates, and mixed fields of particle types relevant to the space radiation environment.

### 11.2 | Macromolecular Endpoints

Focus on early molecular events like DNA damage, oxidative damage 8-hydroxy-2'-deoxyguanosine (8-OHdG) and malondialdehyde (MDA), ATP depletion (mitochondrial dysfunction) which precede cellular changes and are critical to capturing early radiation effects.

### 11.3 | Cellular/Functional Endpoints

Measure outcomes like persistent pro-inflammatory mediators, calcium resorption, cell death (apoptosis, ferroptosis, necrosis)
or synaptic dysfunction to correlate molecular damage with adverse physiological changes.

**12 | Conclusions and Future Direction**

This AOP network has collated decades of research in the radiation field. The biological plausibility for the network is strong, grounded in mechanistic studies that demonstrate the functional progression from specific KEs to AOs, though some uncertainties and limitations remain, particularly concerning quantitative understanding. The network's applicability domain covers relevant taxonomies, life stages, and sexes, ensuring broad relevance across species and developmental stages. Various known space stressors trigger the AOPs, and existing dose and time-response data across KERs provide a foundational understanding of how these stressors propagate biological damage. It is important to acknowledge that early KEs may be initiated, but repair mechanisms and adaptation processes can prevent the full progression to an AO. Progression to clinical diseases depends on various modulating factors, including dose, individual susceptibility, and interactions with other spaceflight-related stressors like microgravity. Therefore, the likelihood of some early molecular disruptions evolving into severe, clinically observable diseases over the course of typical mission durations remains low. The advantage of employing the AOP framework is to identify specific KEs that may be modulated before they escalate into more significant AOs and to have a fully quantitative AOP that can be used to support risk model development.

To achieve this goal, future work may be directed to strengthening the KERs that currently have low evidence in the form of the modified Bradford-Hill criteria and to developing studies using human-relevant models (Figure 6). Some KEs (cellular senescence, epigenetic modifications, mitochondria dysfunction, miRNA expression) that were considered in the original network were ultimately excluded due to a lack of evidence supporting their inclusion (summarized in: Kozbenko et al. 2024; Carrothers et al. 2024; Sandhu et al. 2024; Sleiman et al. 2024). This can be addressed by directing more research toward these potential KEs to explore their roles in the pathway. The AOP network could also be strengthened by considering other factors that underlie the development of KEs to disease such as gene expression changes and epigenetics. Additionally, some of the current KEs are broad (e.g., neural remodeling, endothelial dysfunction) and encompass many different endpoints; these may be further refined in the future to gain a better quantitative understanding of the pathways. Development of standardized guidelines for reporting, evaluating, and presenting data would also benefit AOPs as consistent reporting would make interstudy comparisons between studies feasible. To improve the use of AOPs in the context of space research, future research would require studies using multiple space-relevant radiation types with low-dose chronic exposure in combination with microgravity.

Overall, the proposed AOP network helps to better characterize and assess risk by providing a structured framework to integrate mechanistic data, with the potential to become increasingly informative as quantitative relationships between KEs are developed. The risk characterization will help determine whether

there is a need for risk management strategies. In addition, the collation of information provided by this AOP network will be relevant to the work of international radiation organizations such as the ICRP Task Groups related to space radiation (Task Group on Radiation Protection in Space et al. 2013), the US National Council on Radiation Protection and Measurements (NCRP) and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), particularly as they review the scientific consensus that forms the basis of the system of radiological protection. Lastly, as we enter the next space age, marked by an influx of commercial space missions, such as Inspiration4 and Polaris Dawn, this expanding landscape provides a robust foundation for generating comprehensive data (Mason et al. 2024; Jones et al. 2024; Overbey et al. 2024). These data will be critical in building a complete AOP network to inform and guide future missions with effective risk management strategies.

**Acknowledgments**

All authors contributed to the writing of this paper. The authors wish to acknowledge Vincent Cousineau Daoust (CSA) and Sami Qutob (Health Canada), Katya Feder (HC) for critical review, and Gassan Dhaim (Health Canada) for assisting in finalizing the figures and referencing. Open Access funding provided by the Health Canada library.

**Conflicts of Interest**

The authors declare no conflicts of interest.

**Data Availability Statement**

The data that support the findings of this study are available in aop wiki at https://www.aopwiki.org/. These data were derived from the following resources available in the public domain: https://www.aopwiki.org/, https://www.aopwiki.org/.

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