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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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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). 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. While conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. While different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g., days) compared to apical endpoints from conventional tests (e.g., 90-day sub-chronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Toxicology.)