@entity137 models rely on large amounts of accurate in vivo data. Here, we analyze the quality of in vivo data from the U.S. EPA @entity137 Reference Database (ToxRefDB), using chemical-induced @entity417 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 @entity35 or chronic @entity35 , @entity19 , or @entity235 ). @entity417 was reported most frequently in the @entity35 subchronic studies, followed by chronic studies in @entity235 , @entity35 , and then @entity19 . Concordance between studies for a positive finding of @entity417 (same chemical, different laboratories) ranged from 90% ( @entity35 subchronic predicting @entity35 chronic) to 40% ( @entity19 chronic predicting @entity35 chronic). Concordance increased with manual curation by 20% on average. We identified 49 chemicals that showed an @entity417 phenotype in at least two species. These included 14 @entity9308 moiety-containing compounds that were further analyzed for their potential to be metabolically transformed into substituted @entity24014 , which are known @entity417 -causing chemicals. This analysis should help inform future use of in vivo databases for model development.

Retrospective mining of toxicology data to discover multispecies and chemical class effects: XXXX as a case study.

@entity137 :: ('MESH:D064420', 'Disease') :: ['Toxicity', 'Predictive toxicity'] @entity417 :: ('MESH:D000740', 'Disease') :: ['Anemia', 'anemia'] @entity19 :: ('10090', 'Species') :: ['mouse'] @entity235 :: ('9615', 'Species') :: ['dog'] @entity35 :: ('10116', 'Species') :: ['rat'] @entity9308 :: ('MESH:C023650', 'Chemical') :: ['aniline'] @entity24014 :: ('CHEBI:22562', 'Chemical') :: ['anilines']
@entity417 :: (MESH:D000740,Disease) :: ['Anemia']
