The Toxicity Values Database, ToxValDB, was developed by the U.S. EPA Center for Computational Toxicology and Exposure as a resource to curate, store, standardize, and make accessible a wide range of human health-relevant toxicity information. The database originated in response to the need for harmonized and computationally accessible toxicology data. The scope and design of the database have evolved over time since its first release in 2016. Herein, the newly redesigned structure and development of ToxValDB v9.6.1 is described. The database is a compilation of three classes of summary-level values for chemical substances: in vivo toxicity study results (e.g., lowest-and no-observed adverse effect level), derived toxicity values (e.g., maximum acceptable oral dose), and media exposure guidelines (e.g., maximum contaminant level for drinking water). The current version of the database (9.6.1) contains 242,149 records covering 41,769 unique chemicals from 36 sources (55 source tables). With all records in a consistent structure normalized to a standardized vocabulary, the chemical and data landscape of ToxValDB v9.6.1 can be evaluated. To illustrate chemical coverage, the available data were mapped to chemical lists of regulatory importance. Further, the distribution of oral administered doses within in vivo toxicity studies was assessed by annotated chemical class. The harmonized in vivo data within ToxValDB have many applications including use in chemical screening and prioritization for human health assessment, modeling predictions, and benchmarking for New Approach Methods (NAMs), as well as to address a diverse range of novel research questions.
Adverse Outcome Pathways (AOPs) describe the mechanistic interactions of biological entities with a stressor (chemical, nanomaterial, radiation, virus, etc.) that produce an adverse response. How these interactions and associations are catalogued contributes to our ability to understand mechanistic effects and apply this knowledge to New Approach Methods (NAMs) that have the potential to reduce animal testing in chemical, biological, and material safety assessments. Making AOP data align with FAIR (Findable, Accessible, Interoperable, and Reusable) metadata standards relies on technical tools that implement and process AOP data and related metadata, and the establishment of coordinated and consensus computational bioinformatic methods. Herein current efforts in addressing the FAIRification of AOP mechanistic data and metadata, as well as the international, collaborative efforts to document, and improve the (re)-use and reliability of AOP information will be described. These coordinated efforts contribute to the establishment of a directive for the processing and storing of standardized AOP mechanistic data in the AOP-Wiki repository, and application of these data to next generation risk assessment.
Read-across is a data-gap filling technique used to predict the toxicity of a target chemical based on data from similar analogues. It is predominantly performed through expert-driven assessments which can limit reproducibility and broader acceptance. Data-driven approaches such as Generalised Read-Across (GenRA) offer the potential to generate more reproducible read-across predictions with quantified uncertainties and performance metrics. A key challenge is reconciling expert- and data-driven approaches particularly in how analogues are identified, evaluated and used to derive predictions. A critical aspect of analogue selection lies in understanding the relative contribution of different similarity contexts e.g. whether structural similarity plays a larger role than metabolism similarity. This study explored these considerations by compiling a compendium of expert-driven read-across assessments for repeated dose toxicity endpoints from peer reviewed and grey literature. Pairwise similarity was quantified across structural, physicochemical, metabolic and reactivity features within each case and a prediction model was developed to evaluate the contribution of each similarity context in analogue selection. Although the dataset comprised 157 read-across cases and 695 unique substances, it was limited in size, heterogeneous in origin and variable in analogue selection criteria and use contexts. These factors constrain generalisability of the findings and indicate that conclusions should be interpreted with caution. Nonetheless, the qualitative insight that structure and metabolism were influential led to a followup investigation using graphbased deep learning to explore whether embeddings derived from structure and/or metabolism information could improve read-across predictions, using repeated dose toxicity as a case study, relative to structural similarity baselines.
Read-across is a technique used to fill data gaps for substances lacking specific hazard data. The technique relies on identifying source analogues with relevant data that are 'similar' to the substance of interest (target). Typically, source analogues are identified on the basis of structural similarity but the evaluation of their suitability for read-across depends on other contexts of similarity. This manuscript aimed to review the ways in which source analogues are identified for read-across using chemical fingerprint/scaffold approaches before describing graphbased approaches including; graph kernel, graph embedding, and deep learning. To demonstrate how these could be practically used for analogue identification, five different toxicity datasets of varying size and diversity were selected that had been the subject of previous read-across or QSAR analyses. One dataset was an analogue set whereas the other four datasets comprised substances evaluated for their skin sensitisation, skin irritation, fathead minnow aquatic toxicity and genotoxicity potential. The analogues and their associated similarities using the different graph based approaches were compared with the outcomes from two chemical fingerprint approaches (ToxPrints and Morgan). The results for each dataset are briefly described. Based on the examples evaluated, graph kernel approaches were found to have some promise, in contrast unsupervised whole graph embedding approaches were ineffective for all the datasets evaluated. Graph convolutional networks produced meaningful embeddings for the genotoxicity dataset evaluated. Depending on use case, availability and size of training data, graph similarity approaches have the potential to play a larger role in analogue identification and evaluation for read-across.
New approach methods (NAMs) have been prioritized to reduce the use of animals for chemical safety assessment while continuing to protect human health and the environment. A key challenge of generating toxicity data is the implementation of a standardized analysis approach for transparent and reproducible benchmark concentration (BMC) estimation and uncertainty quantification for assay developers, regulators, and other stakeholders. In this study, we compared the bioactivity results of 321 chemical samples from four established BMC analysis pipelines used for evaluation of developmental neurotoxicity (DNT) NAMs data: the ToxCast pipeline (tcpl), CRStats, DNT DIVER (Curvep and Hill pipelines). We found an overall activity hit call concordance of 77.2% and highly correlated BMC estimations (r = 0.92 ± 0.02 SD), demonstrating generally good agreement across pipelines. Discordance appeared to be explained predominantly by noise within the data and borderline activity (activity occuring near the benchmark response level). Evaluation of the BMC confidence intervals indicated that pipeline selection may impact the estimation of the BMC lower bound. Consideration of biphasic models appeared important for capturing biologically-relevant changes in activity in the DNT battery. Lastly, different approaches to compute 'selective' bioactivity (activity below the threshold of cytotoxicity) were compared, identifying the CRstats classification model as more stringent for classifying selective activity. Overall, these findings indicated greater confidence in NAMs bioactivity results and emphasize the importance of understanding strengths and uncertainties of concentration-response modeling pipelines for informing biological interpretation and application decision making.
