Considerations for application of benchmark dose modeling in radiation research: workshop highlights.

BACKGROUND: Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples. CONCLUSIONS: Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research.

PURPOSE: Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, its application in the analysis of transcriptional data for quantitative adverse outcome pathway (AOP) development is being explored. As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, the number of replicates and dose range on BMD outputs for radiation-induced genes and pathways. MATERIALS AND METHODS: Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to 137Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose range over multiple dose points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs. RESULTS: Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of the number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset. CONCLUSION: Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.

A case example of a radiation-relevant adverse outcome pathway to lung cancer.

BACKGROUND: Adverse outcome pathways (AOPs) describe how a measurable sequence of key events, beginning from a molecular initiator, can lead to an adverse outcome of relevance to risk assessment. An AOP is modular by design, comprised of four main components: (1) a Molecular Initiating Event (MIE), (2) Key Events (KEs), (3) Key Event Relationships (KERs) and (4) an Adverse Outcome (AO). PURPOSE: Here, we illustrate the utility of the AOP concept through a case example in the field of ionizing radiation, using the Organisation for Economic Cooperation and Development (OECD) Users' Handbook. This AOP defines a classic targeted response to a radiation insult with an AO of lung cancer that is relevant to radon gas exposure. MATERIALS AND METHODS: To build this AOP, over 500 papers were reviewed and categorized based on the modified Bradford-Hill Criteria. Data-rich key events from the MIE, to several measurable KEs and KERs related to DNA damage response/repair were identified. RESULTS: The components for this AOP begin with direct deposition of energy as the MIE. Energy deposited into a cell can lead to multiple ionization events to targets such as DNA. This energy can damage DNA leading to double-strand breaks (DSBs) (KE1), this will initiate repair activation (KE2) in higher eukaryotes through mechanisms that are quick and efficient, but error-prone. If DSBs occur in regions of the DNA transcribing critical genes, then mutations (KE3) generated through faulty repair may alter the function of these genes or may cause chromosomal aberrations (KE4). This can impact cellular pathways such as cell growth, cell cycling and then cellular proliferation (KE5). This will form hyperplasia in lung cells, leading eventually to lung cancer (AO) induction and metastasis. The weight of evidence for the KERs was built using biological plausibility, incidence concordance, dose-response, time-response and essentiality studies. The uncertainties and inconsistencies surrounding this AOP are centered on dose-response relationships associated with dose, dose-rates and radiation quality. CONCLUSION: Overall, the AOP framework provided an effective means to organize the scientific knowledge surrounding the KERs and identify those with strong dose-response relationships and those with inconsistencies. This case study is an example of how the AOP methodology can be applied to sources of radiation to help support areas of risk assessment.

Challenges in the quantification approach to a radiation relevant adverse outcome pathway for lung cancer.

PURPOSE: Adverse outcome pathways (AOPs) provide a modular framework for describing sequences of biological key events (KEs) and key event relationships (KERs) across levels of biological organization. Empirical evidence across KERs can support construction of quantified AOPs (qAOPs). Using an example AOP of energy deposition from ionizing radiation onto DNA leading to lung cancer incidence, we investigate the feasibility of quantifying data from KERs supported by all types of stressors. The merits and challenges of this process in the context of AOP construction are discussed. MATERIALS AND METHODS: Empirical evidence across studies of dose-response from four KERs of the AOP were compiled independently for quantification. Three upstream KERs comprised of evidence from various radiation types in line with AOP guidelines. For these three KERs, a focused analysis of data from alpha-particle studies was undertaken to better characterize the process to the adverse outcome (AO) for a radon gas stressor. Numerical information was extracted from tables and graphs to plot and tabulate the response of KEs. To complement areas of the AOP quantification process, Monte Carlo (MC) simulations in TOPAS-nBio were performed to model exposure conditions relevant to the AO for an example bronchial compartment of the lung with secretory cell nuclei targets. RESULTS: Quantification of AOP KERs highlighted the relevance of radiation types under the stressor-agnostic intent of AOP design, motivating a focus on specific types. For a given type, significant differences of KE response indicate meaningful data to derive linkages from the MIE to the AO is lacking and that better response-response focused studies are required. The MC study estimates the linear energy transfer (LET) of alpha-particles emitted by radon-222 and its progeny in the secretory cell nuclei of the example lung compartment to range from 94-5+5 to 192-18+15 keV/µm. CONCLUSION: Quantifying AOP components provides a means to assemble empirical evidence across different studies. This highlights challenges in the context of studies examining similar endpoints using different radiation types. Data linking KERs to a MIE of 'deposition of energy' is shown to be non-compatible with the stressor-agnostic principles of AOP design. Limiting data to that describing response-response relationships between adjacent KERs may better delineate studies relevant to the damage that drives a pathway to the next KE and still support an 'all hazards' approach. Such data remains limited and future investigations in the radiation field may consider this approach when designing experiments and reporting their results and outcomes.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment.

PURPOSE: Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets. MATERIALS AND METHODS: As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type. RESULTS: Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints. CONCLUSION: Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

NASA GeneLab Platform Utilized for Biological Response to Space Radiation in Animal Models.

Background: Ionizing radiation from galactic cosmic rays (GCR) is one of the major risk factors that will impact the health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. The NASA GeneLab project has detailed information on radiation exposure using animal models with curated dosimetry information for spaceflight experiments. Methods: We analyzed multiple GeneLab omics datasets associated with both ground-based and spaceflight radiation studies that included in vivo and in vitro approaches. A range of ions from protons to iron particles with doses from 0.1 to 1.0 Gy for ground studies, as well as samples flown in low Earth orbit (LEO) with total doses of 1.0 mGy to 30 mGy, were utilized. Results: From this analysis, we were able to identify distinct biological signatures associating specific ions with specific biological responses due to radiation exposure in space. For example, we discovered changes in mitochondrial function, ribosomal assembly, and immune pathways as a function of dose. Conclusions: We provided a summary of how the GeneLab's rich database of omics experiments with animal models can be used to generate novel hypotheses to better understand human health risks from GCR exposures.