Innovative Research for a Sustainable Future www.epa.gov/research Richard Judson l email: judson.richard@epa.gov l 919-449-7514 Retrospective Mining of Toxicology Data to Discover Multispecies Effects: Anemia as a Case Study Richard Judson, Charles Wood, Grace Patlewicz, Jeremy Fitzpatrick, Matt Martin U.S. Environmental Protection Agency, Office of Research and Development Introduction Methods Results 1: Database Summary Results 2: Individual Chemicals Conclusions Retrospective inspection of In vivo toxicology data is subject to multiple sources of uncertainty: 1. Lack of control of some key experimental variables: Variability of animals even within the same strain, exact details of diet and animal handling protocols, etc. 2. Statistical issues: Studies are rarely repeated so that experiment-to- experiment variability is rarely characterized. The number of animals used in each dose group may be insufficient to see rare events, etc. 3. Negative results and effects not measured can be hard to distinguish: In guideline studies, specified effects should be examined, and any treatment- related effects recorded. However, this may not be always followed, and some effects not specified by the guidelines may be recorded when seen. 4. Observer bias: When a severe type of toxicity is seen (e.g. a neoplastic lesion), less severe or benign (but treatment-related) effects may be ignored, or not recorded, or recorded in a limited way. Similarly, differences in lesion thresholds among pathologists may influence treatment-related effects. 5. Variability over time in terminology: While this can be managed somewhat by the use of controlled vocabulary onto which the original effect descriptions can be mapped, there are cases where effects are better understood as time goes on and one original term is split into two, so that the exact effects seen in earlier studies are obscured. 6. Transcription errors: In the process of transcribing data from notebooks to initial study reports to published study summaries to a multi-chemical database, there will be transcription errors. 7. Interpretive issues: When data across multiple studies are collated, contextual information such as incidence, severity, dose, or effect groupings may be lost. Goal: Conduct a case study of these issues for chemically-induced anemia. This poster does not necessarily reflect U.S. EPA policy U.S. EPA ToxRefDB Guideline Acceptable CHR (rat, mouse, dog), SUB (rat) 658 chemicals, 1738 studies 441 studies: anemia-positive Harmonize terminology Anemia ≥ 2 / 3 decrease RBC (red blood cell count) HGB (hemoglobin) HCT(hematocrit) Review original reports for all studies in all chemicals with 2 species anemia-positive Clean database: 49 chemicals Studies mechanistically classified Figure 1: Fraction of tested chemicals showing anemia below doses where body-weight decreases are seen (black, bottom), at doses where body-weight decrease is seen (dark gray, middle), and where anemia is not seen (light gray, top). The numbers indicate the total number of chemicals in the database for each species / study-type combination. After manual review DB class Positive Negative Ambiguous Unknown Before manual review Positive 212 2 4 0 Negative 24 128 11 24 Ambiguous 6 2 16 0 Table 1: Concordance between initial database entry and results of manual review. Each cell contains the number of studies in each class before and after the review. Positive (2 or more of RBC, HGB, HCT), Negative (0), Ambiguous (1), Unknown (manual review found that hematology values were not measured in the study). species / study 1 species / study 2 Reproduced Not Reproduced Fraction reproduced rat SUB rat CHR 18 2 0.90 rat CHR dog CHR 13 2 0.87 rat CHR rat SUB 18 4 0.82 rat SUB rat SUB 16 4 0.80 rat SUB dog CHR 11 4 0.73 mouse CHR rat CHR 11 4 0.73 mouse CHR rat SUB 13 7 0.65 dog CHR rat SUB 11 6 0.65 dog CHR rat CHR 13 8 0.62 rat CHR mouse CHR 11 11 0.50 mouse CHR dog CHR 6 6 0.50 rat SUB mouse CHR 13 14 0.48 dog CHR mouse CHR