Scientific Context Approach Results and Applications Case Study Name: Estrogen Receptor Model • Manuscript – Methods & ER Case Study: Internal Review • Manuscript – AR Case Study: Submitted • Manuscript – Thyroid Peroxidase Case Study: In Prep • Manuscript – Cytotoxicity: In Prep • Manuscript – Zebrafish: In Prep • R Package – Toxboot: Published • R Package – Toxpath: In Prep • Data to be available on dashboard and FTP Case Study Name: Androgen Receptor Model References Disruptive Innovation in Chemical Evaluation Assessing Uncertainty in Risk Assessment Models Eric D Watt1,2 and Richard S Judson2 1Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 2National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina epa.gov/research CSS BoSC Meeting 2016 Fig 2 Bootstrap. A) Experimental response values (circles) and hill model fit. B) Uncertainty in response values and fitted model using 1000 bootstrap resamples. Experimental (cyan) and bootstrap resampled (black) response values (circles) and hill model fits (lines). Fig 3 Bisphenol AF ATG_ERa_TRANS_up bootstrap results. Potency (AC50) values for A) Hill, B) Gnls, C) Winning model potency (hill red, gnls blue). D) Correlation between winning model efficacy (top) and AC50 (hill red, gnls blue). E) Experimental values (black circles) and model fit (black curve). Dashed line is 3x baseline median absolute deviation and solid line is assay activity cutoff. 1000 bootstrap fits are indicated (534 hill red, 466 gnls blue). F) Hit call and model selection for all chemicals. Black bars indicate chemical not a hit, red is hill model active, blue is gnls active. Fig 1 ToxCast models. A) Constant (cnst), B) Hill, and C) Gain-Loss (gnls) models. Over 2.6 million in vitro curves • Many chemicals (> 8,000 unique) • Many assays (> 800) Broad assay coverage • Numerous assay sources (> 10) • Many biological pathways (> 400) • Representing many species including human, rat, mouse, and fish • Diverse detection methods including fluorescence, colorimetric, radioactive, electronic sensing, and RNA transcription Broad chemical coverage • Pesticides, food additives, green alternatives, endocrine reference compounds, water contaminants, fragrances, etc. ToxCast Pipeline offers consistent analysis • Multiple models fit to data to determine efficacy (top) and potency (AC50) (Fig 1) • Model selection based on AIC from model fits, hit call based on efficacy relative to cutoff for winning model Fig 7 AR model AUC. Androgen receptor model AUC values for chemicals with an agonist or antagonist AUC > 0.05 with point estimates (circles) and 95% confidence intervals (error bars) for agonist (red) and antagonist (black) values. Fig 9 Androgen receptor model uncertainty distributions. Distributions of agonist (red), antagonist (black) or combined (orange) AUC values are explored using cumulative distribution function plots. A) Equilin is clearly active with some uncertainty between agonist and antagonist modes. B) Prodiamine antagonist AUC is slightly above the cuttoff and narrow distribution around this value showing high confidence in the calculated score. C) Benzoin agonist and antagonist AUC point estimates are 0, but there is ~60% probability of an antagonist value in the range of 0.2-0.35, flagging this chemical as a potential false negative. Fig 8 Androgen antagonist activity shift. Comparison of androgen antagonist assay potency with high (black) and low (gold) agonist concentration. Chemicals acting as true antagonists are expected to see a potency shift. Using bootstrap confidence intervals, we determine which chemicals have a significant potency shift and are therefore likely to be true antagonists. Anticipated Products 1. Federal Register. Use of High Throughput Assays and Computational Tools; Endocrine Disruptor Screening Program; Notice of Availability and Opportunity for Comment. 2015 Jun pp