Regulatory agencies rely upon rodent in vivo acute oral toxicity data to determine hazard categorization, require appropriate precautionary labeling, and perform quantitative risk assessments. As the field of toxicology moves toward animal-free new approach methodologies (NAMs), there is a pressing need to develop a reliable, robust reference data set to characterize the reproducibility and inherent variability in the in vivo acute oral toxicity test method, which would serve to contextualize results and set expectations regarding NAM performance. Such a data set is also needed for training and evaluating computational models. To meet these needs, rat acute oral LD50 data from multiple databases were compiled, curated, and analyzed to characterize variability and reproducibility of results across a set of up to 2,441 chemicals with multiple independent study records. Conditional probability analyses reveal that replicate studies only result in the same hazard categorization on average at 60% likelihood. Although we did not have sufficient study metadata to evaluate the impact of specific protocol components (e.g., strain, age, or sex of rat, feed used, treatment vehicle, etc.), studies were assumed to follow standard test guidelines. We investigated, but could not attribute, various chemical properties as the sources of variability (i.e., chemical structure, physiochemical properties, functional use). Thus, we conclude that inherent biological or protocol variability likely underlies the variance in the results. Based on the observed variability, we were able to quantify a margin of uncertainty of ± 0.24 log10 (mg/kg) associated with discrete in vivo rat acute oral LD50 values.

Humans are exposed to large numbers of chemicals during their daily activities. To assess and understand potential health impacts of chemical exposure, investigators and regulators need access to reliable toxicity data. In particular, reliable toxicity data for a wide range of chemistries are needed to support development of new approach methodologies (NAMs) such as computational models, which offer increased throughput relative to traditional approaches and reduce or replace animal use. NAMs development and evaluation require chemically diverse data sets that are typically constructed by incorporating results from multiple studies into a single, integrated view; however, integrating data is not always a straightforward task. Primary study sources often vary in the way data are organized and reported. Metadata and information needed to support interoperability and provide context are often lacking, which necessitates literature research on the assay prior to attempting data integration. The Integrated Chemical Environment (ICE) was developed to support the development, evaluation, and application of NAMs. ICE provides curated toxicity data and computational tools to integrate and explore available information, thus facilitating knowledge discovery and interoperability. This paper describes the data curation workflow for integrating data into ICE. Data destined for ICE undergo rigorous harmonization, standardization, and formatting processes using both automated and manual expert-driven approaches. These processes improve the utility of the data for diverse analyses and facilitate application within ICE or a user’s external workflow while preserving data integrity and context. ICE data curation provides the structure, reliability, and accessibility needed for data to support chemical assessments.

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