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Retrospective Mining of Toxicology Data to Discover Multispecies and Chemical Class Effects: Anemia as a Case Study
Citation:
Judson, Richard S., M. Martin, G. Patlewicz, AND Charles E. Wood. Retrospective Mining of Toxicology Data to Discover Multispecies and Chemical Class Effects: Anemia as a Case Study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, 86:74-92, (2017).
Impact/Purpose:
• Agency Research Drivers - U.S. EPA Program and Regional Offices are often tasked with addressing the potential hazard(s) of chemicals for which little-to-no data exist. In such contexts, models where toxicity is predicted from chemical or mechanistic data data are used to assess risk. • Science Challenge – Predictive toxicity models (e.g., read-across, computational models) rely on in vivo data. Inconsistencies in the in vivo data can limit the predictive power of developed models, yet variability and reproducibility in these data are largely uncharacterized. • Research Approach – Here, we analyze the quality of in vivo data from the Toxicity Reference Database (ToxRefDB), using chemical-induced anemia as an example. • Results –Within ToxRefDB, we used data on 658 chemicals tested in one or more of 1738 studies. Replication of a positive finding for anemia (same chemical, different laboratories) ranged from 90% to 40%. Reproducibility was improved by manual curation, increasing 20% on average. • Anticipated Impact/Expected use – This analysis should help inform future use of in vivo databases for predictive model development.
Description:
Predictive toxicity models (in vitro to in vivo, QSAR, read-across) rely on large amounts of accurate in vivo data. Here, we analyze the quality of in vivo data from the Toxicity Reference Database (ToxRefDB), using chemical-induced anemia as an example. Considerations include variation in experimental conditions, changes in terminology over time, distinguishing negative from missing results, observer and diagnostic bias, and data transcription errors. Within ToxRefDB, we use hematological data on 658 chemicals tested in one or more of 1738 studies (subchronic rat or chronic rat, mouse, or dog). Anemia was seen most frequently in the rat subchronic studies, followed by chronic studies in dog, rat, and then mouse. Replication of a positive finding for anemia (same chemical, different laboratories) ranged from 90% (rat subchronic predicting rat chronic) to 40% (mouse chronic predicting rat chronic). Reproducibility was improved by manual curation, increasing 20% on average. We identified 49 chemicals that showed an anemia phenotype in at least two species. These included 14 aniline moiety-containing compounds that were further analyzed for their potential to be metabolically transformed into a substituted aniline, which are known anemia-causing chemicals. This analysis should help inform future use of in vivo databases for model development.
